Offshoring Bias in U.S. Manufacturing: Implications for Productivity and Value Added**

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Abstract:

The rapid growth of offshoring has sparked a contentious debate over its impact on the U.S. manufacturing sector, which has recorded steep employment declines yet strong output growth--a fact reconciled by the notable gains in manufacturing productivity. We maintain, however, that the dramatic acceleration of imports from developing countries has imparted a significant bias to the official statistics. In particular, the price declines associated with the shift to low-cost foreign suppliers are generally not captured in input cost and import price indexes. Although cost savings are a primary driver of the shift in sourcing to foreign suppliers, the price declines associated with offshoring are not systematically observed; this is the essence of the measurement problem. To gauge the magnitude of these discounts, we draw on a variety of evidence from import price microdata from the Bureau of Labor Statistics, industry case studies, and the business press. To assess the implications of offshoring bias for manufacturing productivity and value added, we implement the bias correction developed by Diewert and Nakamura (2009) to the input price index in a growth accounting framework, using a variety of assumptions about the magnitude of the discounts from offshoring. We find that from 1997 to 2007 average annual multifactor productivity growth in manufacturing was overstated by 0.1 to 0.2 percentage point and real value added growth by 0.2 to 0.5 percentage point. Furthermore, although the bias from offshoring represents a relatively small share of real value added growth in the computer and electronic products industry, it may have accounted for a fifth to a half of the growth in real value added in the rest of manufacturing.

1. Introduction

Developing economies have become the new, low-cost suppliers of
a wide range of products purchased by consumers and used as
intermediate inputs by producers, with China--now the largest
exporter to the United States--accounting for about a third of the
growth in commodity imports over the last decade.1 The
rapid growth of offshoring--defined as the substitution of imported
for domestically produced goods and services--has sparked a
contentious debate over its impact on the U.S. manufacturing
sector, which shed 20 percent of its employment, or roughly 3.5
million jobs, from 1997 to 2007. Concerns over employment losses
and the trade deficit have prompted a recent spate of government
and private sector proposals to revitalize manufacturing.2

In spite of the steep employment declines and numerous plant
closures, official statistics indicate that output growth in the
U.S. manufacturing sector was robust: real value added grew at an
annual rate of 3 percent, only slightly less than the 3.1 percent
rate for all nonfarm business, from 1997 to 2007.3 These
disparate trends--steep employment declines and strong output
growth--can be reconciled by the notable gains in manufacturing
productivity.

Our paper highlights the dramatic growth of offshoring and the
structural changes occurring in manufacturing in the decade prior to the current
recession. During this time, more than 40 percent of imported manufactured goods were used as
intermediate inputs, primarily by domestic manufacturers. We examine the contributions to the real
growth in domestic shipments in manufacturing from the inputs to
production and from multifactor productivity and find substantial
evidence of offshoring. The contribution from imported materials to
the growth in real manufacturing shipments was larger than that of
any other factor input and was more than twice the contribution
from capital. At the same time, contributions from domestic
materials and from labor were negative.

We maintain that the dramatic acceleration of imports from
developing countries is imparting a significant bias to official
statistics.4 Price declines associated with the
shift to low-cost foreign suppliers generally are not captured in
price indexes. The problem is analogous to the widely discussed
problem of outlet substitution bias in the literature on the
Consumer Price Index (CPI).5 Just as the CPI fails to capture lower
prices for consumers brought by the entry and expansion of big-box
retailers like Wal-Mart, import price indexes and the intermediate
input price indexes based on them generally do not capture the
price drops associated with a shift to new suppliers in China and
other developing countries. The bias to the input price index will
be proportional to the growth in share captured by the low-cost
supplier and the percentage discount offered by the low-cost
supplier (Diewert and Nakamura, 2009). If growth in the input price
index is overstated, productivity and real value-added will also be
overstated.

The necessary conditions for this bias, a substantial shift
towards foreign sources and the existence of significant discounts
on intermediate inputs, were both present in the decade preceding
the recent recession. Moreover, the shift in the import composition
towards developing countries, especially China, accelerated during
the downturn as consumers and businesses have become increasingly
price sensitive.6 Using confidential microdata on
foreign non-oil materials intermediates, we present estimates of
substantial offshoring by manufacturers.7 We find that non-energy
materials inputs from domestic sources actually fellwhile foreign
non-energy materials inputs to manufacturing expanded nearly fifty
percent--to 25 percent of all materials inputs--from 1997 to 2007.
Moreover, developing- and intermediate-income countries accounted
for almost all of this growth in import market share, with
developing countries, mainly China, accounting for over half of the
pick-up.

Although cost savings are a primary driver of the shift in
sourcing to foreign suppliers, the price declines associated with
offshoring are not systematically observed; this is the essence of
the measurement problem.8 To gauge the magnitude of these
discounts, we draw on a variety of evidence from import price
microdata (IPP) from the Bureau of Labor Statistics (BLS) ,
industry case studies, and the business press. Industry case
studies and the business press generally put the magnitude of the
discount from offshoring general manufactured products to
developing countries, such as China, at about 30 to 50 percent, and
the discount to intermediate countries, such as Mexico, at 20 to 30
percent for auto parts.

Using BLS import price microdata, we examine differences in
prices for detailed commodities from advanced versus developing
countries and from advanced versus intermediate countries, as a
proxy for the discounts between the United States and developing
and intermediate countries. As an alternative approach, we employ a
structural model that endeavors to control for any systematic
differences in product quality among countries.9 We also examine
price differentials from observations where importers appear to
have shifted sourcing of a specific product from a supplier in an
advanced country to one in a developing or intermediate country.
Overall, our estimates of price differentials from the import price
microdata are quite consistent with evidence from case studies and
the business press.

As shown in Figure 1, differences in the growth rates of the
price indexes used by the Bureau of Economic Analysis (BEA) to
deflate intermediate materials inputs are indicative of a possible
measurement problem. If price indexes were accurately capturing the
cost savings to businesses that presumably underlie the recent
share growth of imported intermediates, markets clear, and
elasticities of substitution between foreign and domestic
intermediate inputs are greater than one, then the growth of the
import price index should be slower than the domestic price
index, indicating a fall in the price of imported relative to
domestic inputs.10 Instead, the foreign price deflator
for intermediate materials rose faster than the domestic
deflator.11 The differential between foreign and
domestic materials price deflators is especially apparent beginning
in 2002, coincident with the rapid rise of imports from China.

To assess the implications of biases to the input price deflator
for manufacturing productivity and output measures, we implement
the bias correction to the input price index developed by Diewert
and Nakamura (2009), using a variety of assumptions about the
magnitude of the discounts from offshoring. We then incorporate the
alternative measures of real intermediates into a growth accounting
framework and estimate the bias to multifactor productivity and
value added.

We provide estimates not only for the aggregate manufacturing
sector but also for the 19 three-digit NAICS manufacturing
industries. The disaggregate results highlight the fact that one
industry--computers and electronic equipment (NAICS 334)--accounted
for most of the growth in productivity and real value added in
manufacturing over the decade, although that industry accounted for
less than 10 percent of manufacturing's employment and nominal
value added.12 Moreover, if the evidence from
industry case studies, the business press, and the micro import
data is representative of the actual discounts manufacturers
realized from offshoring, our work implies that from 1997 to 2007
multifactor productivity growth in manufacturing was overstated by
0.1 to 0.2 percentage point and real value added growth was
overstated by 0.2 to 0.5 percentage point. Furthermore,
although the bias from offshoring represents a relatively small
share of real value added growth in the computer and electronic
products industry, it may have accounted for a fifth to a half of
the growth in real value added in the rest of manufacturing.

We also implement an alternative bias correction to the import
price index that only accounts for shifts in sourcing of non-energy
materials inputs from advanced to developing and intermediate
countries (i.e. the shifts from domestic to foreign suppliers is
ignored). This more limited examination of biases suggests that
shifts in sourcing among foreign suppliers could have resulted in
up to a 22 percent bias in multifactor productivity growth over the
decade.

Our paper is closest in spirit to recent work by Feenstra,
Mandel, Reinsdorf and Slaughter (2009), who document the effect of
various biases in published statistics for aggregate output and
productivity and find that that measurement problems, which are
tantamount to under-reported terms of trade gains, create a
significant upward bias to measured output and multi-factor
productivity growth in the United States. In this paper, which
focuses on the manufacturing sector at a detailed level, we capture
an additional source of bias via the level changes in input prices
that are realized by U.S. producers when they offshore intermediate
inputs or shift sourcing among foreign countries.

The paper proceeds as follows. The next section presents
additional background on the current state of manufacturing in the
United States. Section 3 reviews data sources and our growth
accounting framework and presents a baseline set of growth
accounting estimates. Section 4 provides an overview of BLS prices
and discusses the biases that may arise from offshoring, while
section 5 provides evidence on the import discount from IPP
microdata and case studies. Sections 6 and 7 present our results on
offshoring bias to manufacturing productivity and value added,
respectively. Section 8 concludes.

2. Background: International Trade and the State of American
Manufacturing

One of the most important developments in the U.S. economy in
recent years has been the rapid growth of trade.13 After
being little changed in the early 1990s, the total value of imports
and exports of goods and services jumped from roughly 20 percent of
U.S. GDP to 28 percent prior to the recent downturn.
Importantly, roughly 80 percent of the increase was attributable to
a run up in the value of imports. The growth of non-oil imports was
the most important contributor to the increase during this period,
and non-oil goods imports--largely manufactured goods--accounted
for almost half of total import growth, while oil accounted for
about a third and services for the remainder of the growth.14

The surge in the imports of manufactured goods--more than 100
percent from 1997 to 2007--reflects both an increase in the import
share of goods for final consumption and the import share of
intermediate inputs, which is our focus in this paper. According to
BEA, the share of intermediate material inputs used by
manufacturers that was imported increased from under
17 percent in 1997 to 25 percent in 2007. Figure 2 plots this
substantial shift in the sourcing of intermediates from domestic to
foreign suppliers.15

Low-wage countries accounted for the most of the growth in
imported intermediate inputs. In figure 2, we categorize countries
into one of three groups--developing, intermediate, and
advanced--based on the country's per capita GDP in 2008.16
Developing countries accounted for half of the growth in foreign
materials inputs, with much of that growth coming from China.
Intermediate countries, such as Mexico, accounted for about a third
of the growth.

How has the U.S. manufacturing sector performed given the growth
of imports from low-wage countries? In particular, has the
substantial shift in sourcing "hollowed out" manufacturing or
instead contributed to the emergence of a leaner, more efficient
industrial sector? On the one hand, dramatic drops in employment
and plant closures portray a sector in decline. The precipitous
decline in manufacturing employment since the late 1990s is evident
in figure 3A, and is coincident with the rise in foreign sourcing.
Employment never rebounded after the 2001-2002 recession as it had
following previous downturns. Indeed, in the decade leading up to
the current recession, manufacturing employment declined by 20
percent, while manufacturing's share of employment in the economy
fell from 14 percent in 1997 to 10 percent in 2007 (figure 3B).
Naturally, plant closures accompanied the employment declines, and
for more recent data, the net number of manufacturing
establishments fell by 10 percent from 1998 to 2007 (Table
1).17 At the same time, the nominal share
of manufacturing value added in GDP fell from 15.4 percent in 1997
to 11.7 percent in 2007.

Statistics on manufacturing production, however, paint a much
more favorable picture of the sector. From 1960 to 2009, the
average annual rate of change in real nonfarm business output was
3.5 percent, only slightly higher than the 3.2 percent annual
change for manufacturing.18 More recently, from 1997 to 2007,
the annual growth rate of real value added in manufacturing was 3.0
percent, almost the same as the 3.1 percent growth for all private
industry. Moreover, cross-country comparisons show larger output
gains in U.S. manufacturing relative to other advanced industrial
countries, according to OECD data.

The divergent trends of employment declines and plant closures,
on the one hand, and rapid growth in real value added, on the
other, are primarily reconciled through the lens of productivity.
The steadily increasing series in Figure 3B shows the ratio of
output per hour in manufacturing to output per hour in all nonfarm
business since 1960; the series indicates that labor productivity
grew considerably faster in manufacturing throughout the period.
From 1997 to 2007, the average annual growth rate of labor
productivity in manufacturing was 4.1 percent compared to 2.7
percent for all nonfarm business. Manufacturing labor productivity
also grew substantially faster in the United States than in most
other major industrialized countries during this decade.19 The
rapid growth in labor productivity has more than offset the
declines in labor input and has permitted firms to sustain robust
growth in real value added.

Analysts have pointed to the robust output and productivity
growth to argue that the manufacturing sector is healthy.20 Our
work, however, suggests that the story is more complex. The
aggregate numbers are unrepresentative of the trends in most of
manufacturing. Moreover, we find that the performance of U.S.
manufacturing has been overstated to some extent in the official
statistics because of offshoring.

3. The Role of Imported Intermediate Materials in U.S.
Manufacturing: Baseline Growth Accounting Results, 1997-2007

3.1 Data for the Growth Accounting Framework

Data from multiple sources are required to estimate
industry-level multifactor productivity and the contribution of
foreign intermediates to growth. The BEA's GDP-by-industry
accounts, part of their Annual Industry Accounts, provide estimates
of gross output, intermediate inputs, value added, and their
respective chain-type price indexes for 61 private industries and 4
government classifications.21 For our analysis, we focus on the 19
manufacturing industries contained in the GDP-by-industry accounts.
To further decompose intermediate inputs, we use the BEA's
industry-level KLEMS account, which decompose intermediate inputs
into estimates of energy, materials, and purchased-services.
22 The KLEMS data contain estimates in
current dollars along with their corresponding quantity and price
indexes.

Industry-level capital input was derived from BEA's detailed
Fixed Assets Accounts. In order to measure the productive services
of an industry's capital stock, asset-by-industry capital stocks
are aggregated using ex-post rental prices following the
Jorgenson-Griliches (1967) approach used by BLS. Instead of simply
chain aggregating the value of capital stocks, this approach
aggregates with the goal of appropriately weighting various stocks
with respect to their productive capabilities. The detailed capital
asset types are aggregated into two components, information
technology (IT) and other capital (equipment, structures, and
inventories).23

Labor input is measured as the change in hours worked by all
persons (employees and self employed) at the GDP-by-industry level
with no explicit differentiation by characteristics of workers. As
discussed in Corrado, et al (2007), the underlying source data on
employment and hours contain serious breaks and inconsistencies due
to the introduction of NAICS. We adopt their methods for deriving
industry level labor input series whose changes are both consistent
over time and appropriate for calculating productivity.24 To
better control for the effects of worker heterogeneity on labor
input and productivity, we also differentiate hours worked
implicitly using the very detailed information on industry-level
employment and payrolls from the Census Bureau's County Business
Patterns (CBP) file.

Lastly, we employ data on imported intermediates and their
respective prices, which we obtain from a combination of published
and unpublished BEA sources.25 The values and prices are available
at the six-digit Input-Output (I-O) commodity level.26 The
BEA calculates the value of imported commodities used by each
industry by assuming that each industry uses imports of a commodity
in the same proportion as the overall ratio of imports-to-domestic
supply of the same commodity.27 This assumption, known as the
"import comparability assumption," has been used in numerous
studies, starting with Feenstra and Hanson's outsourcing work
(1996, 1998).28

The BEA also provided us with detailed imported commodity price
indexes, which it constructs using a concordance between BLS's SITC
import price indexes and BEA's commodity (item) codes. When there
is not a concordance between the BLS price measures and the BEA
commodity codes, the BEA constructs its own end-use import price
index. Taken altogether, we have data on 272 imported commodities,
representing more than half of the approximately 500 detailed BEA
commodity codes.29

3.2 The Growth Accounting Framework

The traditional neoclassical growth accounting framework
provides a useful tool to measure the effects of import price
mismeasurement on multifactor productivity (MFP). Growth accounting
decomposes the sources of growth among the factors that drive
economic activity, i.e., capital, labor, both domestic and imported
intermediate inputs, and MFP--which is estimated residually and
represents the returns to all factors of production.30
Growth accounting is a useful mechanism not only for measuring the
contribution of imported intermediates , but also for assessing the
extent to which mismeasured import and input prices affect MFP.
Specifically, by adjusting published estimates of import and input
prices to account for the bias from offshoring, we can derive
alternative estimates of the contribution to output growth from
intermediate inputs, and hence re-estimate MFP. In what follows, we
employ a gross output approach to measure the contribution of
imported intermediates to economic growth which, as opposed to the
sectoral output approach favored by BLS, more fully accounts for
the substitution between intermediate inputs at the detailed
industry level.31

Growth rates are denoted with hat-notation, where
denotes the real
growth rate of y. Let k denote an industry, sector, or any
other aggregation over industries. In order to estimate
industry-level multifactor productivity we use industry-level
growth rates for gross output,
, and
industry-level growth rates for the production inputs, i.e. labor
, capital
, and
intermediate inputs
.32

Total intermediate inputs at the industry level, Mk, are
decomposed into domestically supplied materials, imported
materials, and purchases of energy and services,

where and denote domestic
and foreign materials, and Ek and Sk are energy and
services, respectively.33 For this decomposition of total
intermediate materials use,
, is the total value of materials
inputs, MATk, for industry k. As mentioned
previously, data on total materials use, purchased energy, and
services comes from BEA's industry-level KLEMS accounts. We then
split total materials inputs into domestic and foreign components
using BEA's imported commodities matrix.

We also define the cost shares for each input for industry
k (,,
,
), as the factor cost to
the total cost for all input factors for industry k. We use
these cost shares to construct weights for our growth accounting
model, where an input's weight for industry k is a
two-period average of the input's cost share in industry k.
As the labor input used for our MFP estimates is the hours worked
of all persons in the industry, we adjust the labor cost
by the ratio of all hours
to employee hours, i.e., the adjusted labor share is:
.34 The share of intermediates,
, can be further
decomposed using the KLEMS categories and the value of imported
intermediates into the shares of imported materials domestic
materials, energy and services,
.
Lastly, the share of capital is calculated as a residual,
.

Chained price indexes for gross output, value added,
intermediate inputs (materials, energy, and services) are available
from BEA's annual industry accounts at the GDP-by-industry level.
To calculate the real growth of domestic materials inputs, we
derive a domestic materials price index using total materials
prices from the industry accounts and commodity-level data on
imports and import prices provided to us by the BEA. Given prices
and nominal values for total materials and imported materials, the
price index and nominal values for domestic materials purchases
() are calculated by chain stripping the
real value of imported intermediates from the real value of total
materials.35

Given the aforementioned definitions, we define productivity
growth as:

(1)

We generate estimates for each of the 19 GDP-by-industry
manufacturing industries, and then aggregate these results to
obtain estimates for the entire manufacturing sector, for the
durable and nondurable manufacturing subsectors, and for several
aggregates excluding computer and electronic products.

3.3 Baseline Results, 1997-2007

The baseline estimates presented in this section are derived
from unadjusted data and are intended to serve as a reference for
the alternative estimates we derive in section 6, which adjust the
official input and import price series for offshoring bias.36 As
shown in figure 2, the import share of materials intermediates
jumped over the 10-year period we analyze and reached
25 percent of materials use in 2007. In addition, the value of
total materials--both domestic and foreign--used by the
manufacturing sector expanded over this period. In other words, not
only did the total amount of purchased materials used by U.S.
manufacturers increase during this period but the composition of
these inputs changed substantially as well. The shift towards
materials, particularly imported materials, suggests there have
been substantial changes in the relative importance of the various
factors of production in terms of their contributions to output
growth. We discuss below how imported materials appear to have
contributed substantially to the growth of most manufacturing
industries. In contrast, domestic materials appear to have made
consistently negative contributions.

Table 2 provides our baseline growth accounting results.37 The
first column presents the average annual rate of growth for real
gross output from 1997 to 2007. The sources of growth- including
MFP, capital, labor, energy, services, and domestic and foreign
materials-appear in columns 2 though 8. The contributions from MFP
and each factor input sum to the growth in gross output. The first
row in Table 2 reports the decomposition for manufacturing as a
whole, while the subsequent rows show decompositions for
manufacturing excluding computers and electronic products,38
durable and nondurable manufacturing, durable goods excluding
computers and electronic products, and the 19 individual
GDP-by-industry manufacturing sectors.

We estimate that real output growth in the manufacturing sector
averaged 1.2 percent between 1997 and 2007, with most of the output
gains driven by the durable goods producing sector, and, in
particular, the computer and electronic products industry. Although
contributions from MFP, capital, services, and foreign materials
all played important roles over the time period of interest, the
sources-of-growth notably vary across industries. In particular,
while MFP growth is a primary contributor to output gains in all
industries, the contributions from capital and services inputs,
though on balance positive, were negative in a number of
industries. . On the other hand, the contribution to growth from
labor, energy, and domestic materials were negative were negative
for manufacturing overall and for almost all industries.

Columns 7 and 8 in Table 2, which show the contributions to
growth from intermediate materials, provide a clear picture of the
rapid pace of structural change currently underway in U.S.
manufacturing. During the period, the contribution of domestically
supplied materials inputs fell, while that of imported materials
inputs greatly expanded, reflecting the substitution of foreign for
domestic intermediate inputs.39 For all manufacturing, the
contribution of imported materials inputs to output growth was
greater than that of any other factor of production and was more
than double the contribution from capital. For manufacturing
excluding the computer industry, imported materials account for 60
percent of the growth during this period.

Looking across the individual industries, many appear to have
cut back on domestic intermediates while simultaneously boosting
their use of foreign intermediates. More specifically, domestically
purchased materials contributed positively to output growth in only
7 of the 19 manufacturing industries, with a substantial
contribution found in nonmetallic minerals, miscellaneous
manufacturing, petroleum and coal products, and plastics and rubber
products. By contrast, the purchases of foreign intermediate
materials contributed positively in all but two manufacturing
sectors (textiles and apparel).

Nevertheless, our baseline growth accounting results present a
picture in which MFP is the predominant contributor to output
growth in the U.S. manufacturing sector; MFP growth averages 1.3
percent for all manufacturing, more than 2 percent for durables,
and about 0.5 percent for nondurables producing industries.
For all manufacturing, the contribution to real output growth from
MFP actually exceeds real gross output growth, indicating that MFP
can account for all of the growth in real gross output over
the decade. Capital, purchased services, and materials all play
important, albeit more modest, roles, while the contribution of
labor is negative and large, reflecting the steep employment
declines during the period.

Another striking result in Table 2 is that computer and
electronic products manufacturing--which includes computers,
semiconductors, and telecommunications equipment--accounts for most
of the output and productivity growth in manufacturing over the
period.40 Output and productivity growth in
the computer industry averaged 7.4 and 6.8 percent per year,
respectively, compared to growth of only about 0.5 percent for
output and 0.7 percent for MFP in the rest of
manufacturing.41 The extraordinary productivity and
output growth in computers reflects, to a large degree,
technological improvements of the products produced and output
price deflators that, when properly adjusted for product
improvements, are often falling rapidly.42

Because statistics on labor productivity, defined as output per
hour worked, are widely used in research and policy analyses, it is
also of interest to consider the relationship between labor
productivity growth and offshoring. In the official BLS labor
productivity release, manufacturing output includes imported
intermediates but excludes intermediates sourced from within the
domestic manufacturing sector. As a result, shifts in sourcing from
a domestic to a foreign supplier do not offset each other,
mechanically increasing labor productivity.43 To this point,
Eldridge and Harper (2009) find that imported intermediate
materials explain 20 percent of the growth in manufacturing labor
productivity from 1997 to 2006. We find that the contribution to
manufacturing labor productivity from imported materials inputs
significantly accelerated over the period.

Although Table 2 documents the substantial growth in offshoring
during the period, it nevertheless likely understates the
true magnitude of the phenomenon. Our focus below concerns the
systematic upward bias in the price indexes used to deflate
intermediate materials. We could not account for the measurement of
two additional factors which likely also impart an upward bias: (i)
imported capital inputs, such as computers and machinery, have
exhibited substantial gains in import penetration and (ii) imported
services inputs (i.e. services offshoring) have accelerated in
recent years, albeit from a very low level.44

4. BLS Prices Programs and Price Measurement Problems

4.1 Background on prices programs

Understanding why offshoring results in biases to price
statistics requires some background on the relevant price programs.
The Bureau of Labor Statistics (BLS) constructs separate price
indexes for imports and domestically produced goods. In the
International Price Program (IPP), BLS surveys a sample of
importing establishments on the prices they pay for imports of a
detailed product. To construct the Producer Prices Index (PPI), the
BLS surveys domestic producers on the prices they receive for a
sample of products.45 The Bureau of Economic Analysis
(BEA) then estimates price indexes for industries' intermediate
inputs using the domestic and import price indexes and using
information on each industry's input structure from the
input-output tables. We will now visit each of these three pricing
programs in more detail.

The BLS's IPP program aims to calculate broad and
consistent Laspeyres import and export price indexes. In order to
compute import price indexes, the IPP program selects a sample of
importing establishments and products to be followed in overlapping
5-year periods. The IPP collects `at-the-dock' prices per unit of
imports on a monthly basis for approximately 20,000 items.46
Importers report characteristics of the imported items of interest
and their transaction prices. The unit of observation used to
construct the import price index is the period-to-period
change in the purchase price of a specific item imported by
a specific establishment. 47 Therefore, the first time a product
is sampled at a reporting establishment, its price change is
missing and cannot be used in the construction of the index. If a
change is made to the description of a sampled item or to its trade
factors, which include country of origin, BLS attempts to adjust
for the value of new characteristic. If changes are too large and
adjustments are not feasible, the item is added to the group and
the new series is "linked in" to the index. This means that the
price change between the old product and the new item that replaces
it is dropped when computing the price index, and it is assumed
that the price movements of newly sampled products are the same as
the average price changes of on-going products at the time of its
introduction. Nakamura and Steinsson (2009) note that because
performing hedonic adjustments is extremely expensive, the
procedure is rarely done, and the overwhelming majority of product
replacements in the import prices sample are "linked in". The IPP
uses a modified Laspeyres formula to aggregate across
establishments and detailed product categories, using product
import sales volumes as weights.48

The producer price indexes measure average changes in
prices received by domestic producers for their products. The PPI
is a transaction based pricing metric, with price-determining
variables, such as color, defining different products. The BLS
performs quality adjustment over time, as characteristics change.
If there is a physical change in the product that can be assigned a
value, then the BLS uses various methods to adjust for quality
changes. As with the IPP, the use of hedonic techniques to adjust
for quality changes is rare. If no price is reported by the survey
respondent, the change is imputed as the average change for other
items in the same cell. The PPI is a modified Laspeyres formula,
with aggregation weights constructed from the latest Census of
Manufactures.

The BEA integrates information from the annual and benchmark I-O
accounts, from the GDP-by-industry accounts, and from various price
indexes constructed by BLS to create the National Income and
Product Accounts (NIPAs). An important part of that exercise
involves deflating intermediate purchases in order to
properly measure value added at the industry and sector level.
Using the I-O accounts, BEA estimates the amount of each commodity
used in the creation of each industry's gross output. The I-O
accounts do not distinguish whether intermediate inputs are of
foreign or domestic origin. Therefore, as discussed above, when
constructing a price index to deflate intermediate inputs, BEA
assumes that the fraction of a particular intermediate input that
is foreign is the same across all user industries and that it
equals the import share of all domestic consumption of that
commodity--the import comparability or constant industry
assumption.

BEA generally uses the PPI to deflate the value of domestically
produced intermediate inputs and the import price index from the
International Prices Program to deflate imported intermediate
inputs. In 1996, BEA introduced its own hedonic index to adjust for
quality changes in semiconductors. In addition, BEA further
corrects BLS PPI prices for telecommunications equipment based on
hedonic methods. The resulting commodity quantity indexes are
aggregated up to the industry level via a Fisher index-number
formula and used to calculate a price index at that same level
(Strassner and Moyer 2002).49

4.2 Problems with the price indexes programs

The BLS takes great care to ensure that it is pricing the same
item over time, and thus that price indexes are based on "apples
to apples" comparisons. Conceptually, each observation used in the
construction of a particular price index represents the
period-to-period price change of an item as defined by very
specific attributes and reported by a specific establishment.
Efforts to carefully control for product attributes when collecting
data on price changes lead to two classes of problems in pricing at
the elemental level, both of which have been widely discussed in
the literature on the CPI:50

1) New goods and quality changes. If a new product is
introduced or the attributes of an ongoing product change
significantly, then a price change for the product is missing or
difficult to construct.

2) New supplier. Because the unit of observation is the
price change reported for a specific item by a specific
establishment, price indexes often fail to capture price declines
consumers and businesses experience when they shift purchases to a
new, low-cost supplier. In the literature on the CPI, this problem
was termed outlet substitution bias.

A third class of problems widely discussed in the price index
literature concern the proper aggregation of the sampled
observations of price changes. Composite price indexes must be
constructed, and even if all price changes are accurately measured,
the problem of how to add up "apples and oranges" remains. In
particular, the Laspeyres index, which computes the price change
for a fixed basket of goods, does not allow for substitution by
purchasers among goods over time as relative prices change, and
thus in some cases superlative indexes are preferred. However, when
the price changes themselves are missing or mismeasured, as in the
case of new goods or new suppliers, reweighting the sampled price
changes does not correct this more fundamental problem.51
Although an extensive literature on the implications of these
measurement problems exists for the CPI, their implications for
other price indexes and economic statistics have, until recently,
received relatively little attention. As mentioned earlier,
Feenstra, Mandel, Reinsdorf and Slaughter (2009) estimate biases to
the import price index resulting from the growth in new goods or
product varieties, from the exclusion of tariffs in IPP prices, and
to the fact that the import price index is constructed using a
Laspeyres rather than a superlative index formula. They find that
each of these factors contributes to an index of U.S. terms of
trade being underestimated. That is, similar in spirit to our
results below but for different reasons, official price measures
ascribe what are actually mismeasured terms of trade gains to
productivity growth.

In addition, Nakamura and Steinsson (2009) consider biases to
import and export price indexes as a result of model changes, which
constitute the introduction of new goods or varieties, in imported
and exported commodities--what they term "product replacement
bias." They argue that because price changes associated with model
changes are generally missing, the responsiveness of import and
export prices to exchange rate changes has been much greater than
previously estimated.

Our paper, along with several recent studies, focuses on biases
to price indexes resulting from the shift in sourcing to low-cost
overseas producers.52 We turn now to a fuller examination
of this bias.

4.3 Price biases arising from offshoring and other shifts in
sourcing of inputs

Consider first the problem of measuring the drop in an input
price when an organization shifts its sourcing from a domestic
supplier to a new, low-cost foreign supplier. There may be
considerable lag before a new item is included in the import price
sample, and, as noted above, because indexes are constructed from
observations of price changes of specific items sampled in a
reporting establishment, the price change will be missing when the
item is first sampled. Moreover, to correctly measure the input
price change at the elemental level, the BLS should measure the
price difference between the imported item and the domestic item it
replaces (Alterman 2009, Diewert and Nakamura 2009, Houseman
2008).53

Figure 4 presents a stylized depiction of the problem in the
context of offshoring. The IPP measures the price change from
period t to t+1 of a specific imported product from a
particular importer, and the IPP measures the price change from
period t to t+1 of a specific product produced by a
specific domestic producer. Neither the IPP nor the PPI captures
the price drop (d) that occurs when businesses shift from a
high-cost domestic to a low-cost foreign supplier. The input price
index, as computed by the statistical agencies, is essentially a
weighted average of period-to-period changes measured in the IPP
and the PPI, and thus the price drop from offshoring is missed. The
correct index, however, would capture the period-to-period change
of the average price that U.S. companies pay for each intermediate
input. More rapid introduction of new suppliers into the BLS
sampling frame or more frequent sampling of prices--common
suggestions for improving price statistics--will not address this
particular problem.

Bias in price indexes arising from a shift in sourcing to a new,
low-cost domestic or foreign supplier is analogous to outlet
substitution bias in the CPI literature (Houseman 2008, Diewert and
Nakamura 2009). Building upon Diewert (1998), which characterizes
outlet substitution bias to the CPI, Diewert and Nakamura (2009)
characterize the bias to the input price index from outsourcing and
offshoring. Consider the rate of price increase for an item used as
an input in production. The ratio of the price of that item
reported by a specific producer (or importer) in periods t
and t-1accurately characterizes the rate of price increase
facing purchasers of that input in the absence of shifts in
sourcing of that input. However, if the producer shifts all or some
of its sourcing of the input to a lower-cost provider, the measured
rate of price increase will be upward biased. Following Diewert and
Nakamura (2009, pp. 17-18), the true rate of price increase at the
elemental level may be characterized as follows:

(2)

where P represents the unit value of a homogeneous input,
s is the physical share of the input sourced from the
low-cost supplier, d is the percentage discount offered by the
low-cost supplier, and 1+i is the rate of price increase
from period t-1 to 1 for the high cost supplier (assumed the same
for the low-cost supplier).54

It is commonly believed that biases to price indexes from the
introduction of new goods or--what is observationally equivalent in
the data--the entry of a new supplier of existing goods, are not
large, because at any point in time the number of new goods or new
suppliers is small, and because the market share of new products or
new entrants is small.55 With respect to the first point,
however, recent research points to extraordinarily high product
turnover in the import data (Broda and Weinstein 2006; Nakamura and
Steinsson 2009).56

The second point--that biases to price indexes are small because
market shares of new products or new entrants are small-also may
not hold in the case of offshoring, given, as we have shown
earlier, the large and growing magnitude of international sourcing.
Moreover, the likely presence of sizable information and other
short-run adjustment costs that decline with time implies that low
cost suppliers may continue to expand market share following the
initial entry, even if their prices relative to competing products
do not change. For instance, to explain the existence of large and
persistent cross-country differences in the price of
observationally identical semiconductor wafers, Byrne, Kovak, and
Michaels (2010) hypothesize that firms may respond to new
opportunities to produce semiconductor wafers at lower average cost
overseas with a lag because they have large sunk costs in existing
facilities. More generally, although the dynamic by which low-cost
producers enter and capture market share from incumbents is an
important mechanism by which prices change, it is a dynamic largely
missed in price indexes.57

Recent studies based on the microdata from the IPP also show
considerable rigidity in import prices. In particular, Nakamura and
Steinsson report that 45 percent of items in the IPP register no
price changes during the entire period they are in the sample, and
more than 70 percent have two price changes or less. Whatever
the underlying reason for the rigidity in prices, the stylized fact
is important because if the import price for a particular product
registers most of its relative price change after entering
the U.S. market, such a dynamic, in theory, might be picked up by
the IPP. The growth in market share of low-cost imports from
developing economies no doubt reflects continual productivity gains
in those countries, quality improvements, and declines in
quality-adjusted product prices. Yet the combination of high rates
of product replacement and price rigidity in ongoing products
suggests that the import price index will not pick up this
dynamic.

Widespread shifts in sourcing have occurred not only from
domestic to low-cost foreign suppliers, but also from relatively
high-cost foreign suppliers toward new, low-cost foreign entrants,
as evidenced by the growth in the share of imported inputs from
developing and intermediate countries. Unlike the PPI, the import
prices program surveys the purchaser, rather than the seller, of
the items sampled. Thus, it is possible that a price change
associated with such shifts in sourcing among foreign suppliers
will be captured in the import price index.

The key to capturing the price change is that, when the shift to
the new source occurs, the imported item from the new source is not
treated as a new series but rather as a continuation of the old
item. Suppose, for example, that a manufacturer purchases a
specific part from a wholesale importer that, in turn, shifts its
sourcing of the part to a lower-cost provider in a different
country. A shift in sourcing the item from one country to another
will be flagged as a change in a trade factor, which may trigger
the discontinuation of one series, the introduction of a new
series, and hence a break in the price series. If, however, the
importer confirms that the item from the new source country is
identical to the one it replaces or if it can adjust for any
quality differences, the series will be continued and the price
change from the shift in sourcing will be recorded. If, instead, it
is a different wholesale importer that purchases the item from the
new, low-cost foreign supplier and, in shifting source countries
for its imported parts, the manufacturer simultaneously changes
import wholesalers, the price change will be missed.

5.1 Evidence on share shifts for commodities used as
intermediate materials

In this section we document patterns of changing market share
among domestic and international sources for intermediate inputs at
the level of detailed commodities. As depicted in figure 2, the
aggregate share of imported intermediate goods has increased from
17 to 25 percent over the period 1997-2007, driven largely by
increases in developing and intermediate countries' shares. The
detailed product data underlying these aggregate shares tell a
richer, more intricate story of domestic-international and
intra-foreign share dynamics and permit us to identify
country-product shares that are increasing at the expense of other
country-product shares. In other words, we can determine to which
countries share is accruing and at which countries' expense.

To begin, we consider the shifts in shares among U.S. import
sources. As mentioned, we categorize countries into one of three
groups--developing, intermediate, and advanced. Figure 5 shows the
long differences, i.e., differences calculated over the entire
sample period, in market share of developing- and
intermediate-income countries for 344 manufactured commodities over
the period 1997-2007. 58 It is immediately apparent that most
observations lie to the right of the vertical axis, denoting an
increase in developing country shares; for over 90 percent of
products, developing country shares increased. It is also notable
that in the majority of those cases, developing country gains
outpaced both the gains and losses of the intermediate countries:
observations in the bottom-right quadrant above the
downward-sloping 45 degree line are instances in which developing
country share growth exceeded intermediate share declines, implying
that the share of advanced countries also registered a decline.
Observations in the top-right quadrant below the upward-sloping 45
degree line are instances in which developing country share growth
exceeded intermediate share increases, implying not only that the
share of advanced countries declined but that the developing share
increased relative to intermediate. In the top-left quadrant there
are a few instances in which intermediate share increased at the
expense of developing, and in the bottom-left virtually none in
which advanced increased share at the expense of both intermediate
and developing.

For our implementation of the input price bias correction below
we require a measure of the share of imported inputs. We
thus combine the import shares with information on domestically
produced inputs to compute the input share coming from developing
and intermediate countries. Figure 6 shows the long differences in
the domestically sourced input share as they relate to the combined
growth of developing and intermediate share at the commodity
level.59 Here the vast majority of
observations are in the bottom-right quadrant, denoting a gain in
developing and intermediate share at the expense of a loss of
domestic share. The 45-degree line in this diagram informs us of
the extent to which advanced foreign countries are gaining share
from domestic sources: observations above that line are those in
which developing/intermediate share is growing faster than domestic
share is falling, implying that the advanced input share is falling
as well. Since the share changes line up well with the 45-degree
line, there does not seem to be any large net changes in the share
of inputs sourced from advanced countries. Most of the action
involves the shifting from domestic sources to developing and
intermediate foreign sources.

5.2 Evidence on the Import Discount from IPP Microdata

5.2.1 Overview

As described in equation 2, the formula to correct for import
price bias due to offshoring requires a measure of the discount
('d') offered by foreign input producers relative to
domestic ones. Since there are no direct data sources for this
discount spanning the large number of industries we examine, we
consider three alternatives using microdata collected by the IPP.
First, we examine the relative prices of U.S. imports coming from
low- and intermediate-income source countries compared to those of
advanced countries. Second, recognizing that the composition of
traded varieties across income groups can vary substantially even
within narrowly defined products (which could be driving some of
the price differences observed),60 we examine
price-switching behavior at the more detailed level of U.S.
importing firms. Specifically, we compute the price change when a
given firm switches providers to a new source country, which likely
controls for item specification changes to a greater extent.
Finally, we take a structural approach to adjusting relative prices
for compositional quality differences, using recently developed
methods from the international trade literature. We then compare
our empirical measures of the offshoring discount with evidence
from industry-specific case studies of cross-country cost
differentials in the next section.

5.2.2 Full Sample, Unadjusted Estimates

For our first empirical proxy for d we define the
relative import price from low-wage source countries. These
relatives are constructed at the level of transactions within
narrowly defined product groups over the period September 1993 to
May 2007. When an item enters the IPP sample, a detailed
description is collected and the reporting importer is asked to
update the price for that specifically defined item (i) over time
(t). Items are identified by an array of transaction and product
characteristics, including: country of origin (c), Harmonized
System 10-digit (HS10) product code (j) and the unit of measure
(e.g., pound, kg, container, etc.) in which the sale took place
(u). 61,62

As described above, we separate countries into three groups:
advanced, developing, and intermediate, based on each country's per
capita GDP in 2008 relative to the U.S.: c A
denotes the set of advanced countries; c I
denotes the set of intermediate-income countries; and c D denotes the set of developing countries. The import
price discount for an individual item in the developing set, and
analogously for items from intermediates, is measured as:

(3)

where each d(1) is the
percent difference in price between an import transaction from a
developing country and a geometric mean of advanced country
transaction prices in the same HS10 group, unit of sale and
month.63 The weights in the geometric mean
are wijt is the item-level probability weight used by the
IPP in aggregating to the HS10 product-level. The discount from
(3) can then be
aggregated further using IPP item- and establishment level weights;
for instance,
is the
average discount for developing country c in product j at
time t. Aggregation of
across
time periods and products uses fixed weights; for example, China's
growing market share and compositional shifts into new and larger
product groups do not feed back into a greater weight to China's
differentials.64

The top two panels of figure 7 illustrate the magnitude of
d(1)
for developing and intermediate-income exporters by NAICS 4-digit
product code in the manufacturing sector. The vast majority of
relative prices are negative with an average price difference of 63
percent for the developing group and 58 percent for the
intermediate group. There is a substantial amount of heterogeneity
in the import discount across products: both the left (i.e., food,
beverage, textiles, apparel) and middle (i.e., wood, fuel,
chemicals, plastics, minerals) portions of the product spectrum are
characterized by significant dispersion in both discount magnitude
as well as the difference between developing and intermediate,
whereas the right (i.e., machinery, electronics, semiconductors,
transportation) is characterized by large cross-product variation
but smaller differences between developing and intermediate prices.
In the top-left panel, the size of each bubble is weighted by the
change in input share of developing or intermediate countries. This
weighting shows where the bulk of mass resides for each discount in
our implementation of the price index correction formula. To the
right of the figure, there is a large concentration of developing
country discounts of between 60 and 80 percent and lying
systematically below the intermediate export prices for the same
products. The top-right panel uses the average size (in dollars) of
each industry's imported intermediate input as weights, showing
even greater emphasis on machinery, electronics, semiconductors and
transportation products.

The relative price (HS10) columns of table 3 break out the
import discount of selected countries. For the developing
countries, with the exception of Argentina and some smaller
exporters, all price differences are negative, with notably low
price source countries including Bangladesh, Bolivia, China, India,
Nicaragua, Pakistan and Sri Lanka. For the intermediate countries,
all price differences, save Croatia and Venezuela, are negative,
with notably low price source countries including Hong Kong,
Hungary, Poland and Taiwan.

5.2.3 Switching Estimates

A closer empirical counterpart to the decision of U.S. producers
to switch to input sources from abroad is the decision of U.S.
importing firms to switch among foreign source countries. For
instance, one would expect that trends toward U.S. sourcing in
China would not only correspond to switches away from U.S.
producers but away from Japanese and European producers as well. We
thus identify firms in the IPP sample that have added new source
countries to existing import product categories and measure the
product prices from those sources relative to incumbents. Despite
focusing on a significantly smaller portion of the IPP sample than
the previous discount measure, the firm switching relative price
confers the significant benefit of controlling for cross-firm
variation in import composition. It stands to reason that
heterogeneity in this composition within firms will be lower than
the corresponding measure at the HS10 product level.

A country switch is defined as a new import item in a
firm-HS10-month cell from a country other than where incumbent
items are sourced. In the full sample, in many cases a new item is
observed at the same time that a firm enters the IPP sample; since
these are uninformative about switching behavior they were
discarded, leaving 9,676 instances of new items in incumbent firms
over the course of the sample. Of those, 7,609 new items were from
the same country as an incumbent item.65 The relative prices of
these new items did not vary greatly relative to incumbents, and
were on average 2 percent higher in the developing country set, 3
percent for the intermediate countries and 4 percent for advanced
countries. The remaining 2,067 observations are instances in which
new items came from a new country source. The relative price of a
country switch from an advanced to a developing country is defined
as:

(4)

where the subscript f indexes a specific importing firm and
the product, unit of sale and month subscripts are suppressed for
clarity. Again, w denotes the item-specific weight
constructed by the IPP.

Table 4 shows the average relative prices of these switches in
percent, broken down according to our country classification. For
instance, when a new item sourced from a developing country appears
in a cell containing an advanced country incumbent (the top-right
entry of panel (a)), the average discount is 44 percent; switching
from an advanced to intermediate source confers a discount of 28
percent. To see how these discounts have evolved over time, we
compute the same statistics for the first seven years of the sample
(panel (b)) and the last seven years (panel (c)). Interestingly,
the developing country discount is fairly stable over those periods
while the intermediate-advanced discount is much more pronounced in
the early period.66

Moving back to table 3, the second column displays the
developing and intermediate switching discounts by country,
alongside the average (unadjusted) cross-country price differences
for all imports within an HS10.67 For the larger U.S. trading
partners, the discounts are still significantly negative, though
less so than the unadjusted measures. China's discount drops from
75 percent to 62 percent, India's discount drops from 76 percent to
46 percent, Brazil drops from 45 percent to 12 percent, and Mexico
drops from 54 percent to 15 percent. Overall, the discounts at the
HS10- and HS10-firm-level are correlated positively, however
consistent with the narrative that compositional differences across
items are smaller within importing firms, firm-level switching
discounts tend to be smaller.

5.2.4 Structurally Adjusted Estimates

Finally, we implement a structural estimation to infer the
degree of unobserved compositional differences driving the relative
prices. The objective is to convert observed price differences
across countries into common units of quality, where the remaining
heterogeneity in prices is a `pure' measure of production cost
advantage. For instance, if China has lower average quality
embedded in its products then the unadjusted relative prices will
overstate the benefits to outsourcing to China. In order to discern
quality from cost drivers of price differences, we regress the
cross-country variance of unadjusted prices on estimates of quality
ladder length at the detailed product level. The residual of that
estimation is the component of price variance not due to
variance in quality characteristics.

Our measures of quality ladder length for U.S. imports use the
same method as described in Mandel (2010), which proposes a simple
theory to discern whether firms within a given industry are
competing in price versus quality space. The theory introduces
costs to producing quality characteristics, such that productive
firms endogenously choose to produce higher quality outputs. What
relative price those highly productive firms charge depends on the
balance of: (i) lower prices due to lower costs, and (ii) higher
prices due to producing a good that consumers value more.
Ultimately, this balance depends on the nature of the good; a
product group with little quality differentiation will have
productive (larger) firms selling at lower prices, while a product
type with more scope for quality differentiation will have
productive (larger) firms selling at higher prices.

The identification of quality scope uses IPP micro-data as
described above. A structural equation is derived for the skewness
of U.S. import prices at the product level as a function of the
skewness of source country wages and the skewness of the firm size
distribution. It is the size-price correlation that is informative
about the scope for quality differentiation; the observation of
higher sales at higher prices suggests that there is a significant
degree of heterogeneity in quality characteristics across product
varieties. The price skewness at the HS10 level is measured
directly using IPP import prices while the firm size skewness uses
the export sales size distribution of U.S. exporting firms within
the same category.68 The result is a classification
scheme of products; those with a high correlation of price skewness
and size skewness are classified as high quality scope industries,
while those with a low correlation of price skewness and size
skewness are classified as low quality scope (i.e., more
homogeneous).69

Given quality scope measures, the assumption used to identify
quality differences in relative prices across sources is that the
dispersion in observed item prices is proportional to the
underlying dispersion in quality composition. To be concrete, let
us define a quality-adjusted item price, qi, and some
measure of that item's quality, zi. The quality-adjusted
price is the observed price, pi, normalized by quality to
obtain a comparable measure of price across items: pi=qi*zi.
The variance of (the log of) observed prices within an HS10 group
can then be rewritten as a function of the variance of ln(z) and
ln(q), as follows:

(5)

where
is a geometric mean of
variable x across items within an HS10 group. It is immediate that
if the covariance of quality and quality-adjusted price were to be
zero, then the variance of observed prices would vary one-for-one
with the variance of quality.70 Since no reliable measures exist
for this covariance, and since there are offsetting theoretical
rationale for its sign, we proceed by employing the simplifying
assumption that it is zero. The variance of quality-adjusted prices
from (5) is
then approximated by the difference in the variance of observed
prices and unobserved quality:

(6)

This relationship is implemented in the data by regressing the
relative price of developing and intermediate-income exporters on a
measure of product-level quality variance described above and a
quadratic term for quality variance:

(7)

where the size of the country relative price,
|d(1)kjt|, measures
the degree of intra-product price dispersion.71 The residual of
this expression is our measure of the variance of quality-adjusted
price:
. Table 5
displays regression results for (7) for two sets of
U.S. import product groups.72 For our purposes, the most
conservative estimates to use are those which ascribe the most
observed price variance to quality. With this in mind, we apply
estimates from specification (7) to the
construction of quality-adjusted relative prices as follows:

(8)

The resulting product-level relative prices are illustrated in the
bottom two panels of figure 7. As expected given the positive
relationship between price and quality variance, there is a
pronounced compression of the variance of quality-adjusted prices
relative to the unadjusted set in the top two panels, with
developing and intermediate country relative prices increasing to
about 30 and 15 percent below their advanced country counterparts,
respectively. This large adjustment is driven by low-priced
varieties in long quality ladder industries being given a
correspondingly large boost upwards to account for differences in
specification. For example if China has a relatively low price in a
highly quality differentiated industry, that is indicative in the
model of both low quality and low productivity. In that case,
China's quality-adjusted price would be higher than its unadjusted
price. In most instances, after the quality-adjustment the ordering
of developing versus intermediate country groups is preserved.

The third column in table 3 shows the quality-adjusted relative
price measures by source country. Overall, the developing country
discount is 25 percent and the intermediate country discount is 14
percent. Notably large increases in relative price due to quality
differences occurred in Bangladesh, Bolivia, Costa Rica, China,
India, Kenya, Nicaragua, Pakistan and Sri Lanka. Under the
assumptions of the structural model, this implies that a
significant portion of their discount may be accounted for by
quality differences. For the intermediate set of countries, there
are instances in which the relative price flipped signs due to the
quality-adjustment (i.e., became a premium). The fact that South
Korea is pricing at a premium of 13 percent in quality-adjusted
terms may suggest that our adjustment is conservative in some
cases.73

5.3 Case Study Evidence on the Import Discount

As a means of checking the validity of our three discount
measures, we have compiled several examples of industry case
studies and press articles documenting cross-country input cost
differences (see table 6). These studies often have richer data on
particular product specifications, albeit for a single industry
classification, and can therefore control for quality differences
directly for that industry.

For the developing country set, case study evidence is most
widely available for high-tech products imported from China. A
McKinsey (2006) study cites cost savings from production of
electronic equipment in China of between 20 and 60 percent; for the
narrower product category of semiconductors, Byrne, Kovak and
Michaels (2009) find the savings to be roughly 40 percent, while a
Business Week (2004) article cites 40-50 percent for circuit
boards. All of these estimates are in line with our measures, with
the developing country price differences in the NAICS category 334
(Computer and Electronic Product Manufacturing) bounding the case
study estimates. For general manufactured goods, Business Week
(2004) pins the China discount at 30-50 percent, in line with our
range of 35-60 percent between the firm-level source switches and
the unadjusted estimates.

For intermediate countries, case studies have focused on auto
parts exported from Mexico. Klier and Rubenstein (2009) estimate a
cost discount for aluminum wheels, a highly homogeneous product
category, of 19 percent, while Kennedy (2004) finds the Mexican
discount for auto parts in general to be 20-30 percent.74 Both
of these studies are line with our median firm-level switching
measure of 26 percent. Of note, the quality-adjusted Mexican
premium of 60 percent is an instance in which our structural
quality adjustment is out of line with the industry estimates, and
may have been overly punitive. On the other hand, our
quality-adjusted estimate of the intermediate country semiconductor
discount of 34 percent is closest to that of 24 percent estimated
by Byrne, Kovak and Michaels (2009) for Singapore.

5.4 Implementation of the Diewert & Nakamura (2009)
formula

As discussed above in Section 4, with measures of changing
import shares and the price discount of low-cost foreign suppliers
in hand, Diewert and Nakamura's (2009) formula-based approach can
be implemented to estimate the size of the upward bias in input
prices due to offshoring. Commodity-level input prices were derived
using the price index for high-cost (i.e. domestic) suppliers in
conjunction with information on the import discount and share in
domestic supply of each commodity.75 We implement this
correction at the NAICS 4-digit product (commodity) level to adjust
the materials deflators used for our baseline growth accounting
framework in Section 3.76

For illustrative purposes, let us examine the correction for a
specific industry and year. In 2006, the deflator for the
agriculture, construction, and mining machinery industry (NAICS
3331) increased by 3.5 percent. Concurrently the share of inputs
sourced from developing countries increased by almost 3.5 percent,
priced at an unadjusted discount of roughly 40 percent. Plugging
these numbers into the bias correction formula results in a
(1.035)*(0.035)*(0.4) = 1.4 percent
overstatement of input cost growth, over a third of the measured
change in the deflator. It is the corrected deflator change of
about 2.1 percent for that commodity and period, and subsequently
aggregated to the industry level, that we use to estimate
bias-corrected real output and MFP in the following sections.

From the commodity level, the shipment deflators are aggregated
by industry using each industry's use of the commodity (i.e., its
share in total manufactured materials use) as its weight. The
industry deflator is then combined with the deflator for
non-manufactured components to obtain a measure of the intermediate
materials price deflator (Section 6 provides more detail on this
approach).

In figure 8, we compare the published data to one of our
bias-corrected measures. The vertical distance between each point
and the 45 degree line represents the size of the offshoring bias.
For all manufacturing, we find that cumulative price growth of 20
percent77 over the period 1997 to 2007
overstates the bias-corrected inflation rate by 9 percent when we
adjust input costs with our full sample (unadjusted) estimates of
the foreign price discount. That is, nearly half of the growth in
input costs over that period may be attributable to international
source switching. Also shown are commodity specific cumulative
price changes. Given similar input cost inflation, it is
interesting to note the industry-specific differences in the bias.
For example, electrical equipment had a larger bias than
nonmetallic minerals even though they both had unadjusted deflators
that rose by about 30 percent over the sample period. Similarly,
furniture and primary metals had larger biases relative to printing
and chemicals, respectively. An important category not shown in the
figure, but included in our calculations below, is that of
computers and equipment. That industry had unadjusted price
declines of 35 percent over the ten year period, 51 percent after
adjusting for the offshoring bias.

6. Offshoring Bias and Measured Productivity in U.S.
Manufacturing

6.1 Overview

As discussed in Section 5, we apply our offshoring correction at
the commodity level to construct a range of alternative
input price indexes for U.S. manufacturers. We use these adjusted
input prices to construct industry-level purchased materials
deflators, adopting essentially the same approach as the BEA. In
this section, we update the growth accounting exercise initially
performed in Section 3 using these alternative industry-level
materials deflators to investigate the extent to which offshoring
may have distorted measures of manufacturing productivity. Industry
output, and the contributions of capital, labor, energy, and
services are all unchanged from Section 2. As such, all of the
revisions to multifactor productivity presented below stem from our
adjustments to purchased materials.

To derive our industry-level materials deflators, we first
aggregate over the real values of commodities to obtain the total
real materials use for each of the 19 manufacturing industries in
the GDP-by-industry accounts. Fisher-ideal chained dollar indexes
are constructed in which the weights are each commodity's share of
each industry's total materials use. We then aggregate across
industries to derive Fisher chained dollar materials indexes for
durable and non-durable manufacturing and for all of
manufacturing.

For our baseline estimates, and following the BEA,
imported and domestic commodities are treated as separate inputs
(Strassner and Moyer, 2002). Real values for each imported
commodity are calculated using the confidential data provided to us
by BEA. Domestic commodity values are derived residually based upon
BEA's KLEMS Intermediate Use Estimates, which show the total use of
each commodity by each industry and the values of imported
commodities.78 The deflators for domestic
commodities were provided to the Federal Reserve Board by the BEA.
As discussed in Section 4, although the BEA mostly uses PPIs to
deflate domestic materials, there are a handful of commodities for
which they develop their own price indexes using hedonic
methods.

For our adjusted estimates, as discussed in Section 5,
commodity level input prices were constructed using the price index
for high-cost (i.e. domestic) suppliers in conjunction with
information on the import discount and on import shares. Thus,
under this approach, we no longer distinguish between imported and
domestic commodities when aggregating across commodities to
construct industry level materials deflators. Instead, the real
value for each commodity is derived by deflating the total use of
the commodity (i.e. both imported and domestic) with its
corresponding bias-adjusted input price. Our adjusted approach is
otherwise identical to our baseline approach. In other words, real
industry-level materials use is derived as a Fisher-ideal chained
dollar aggregate derived from real commodity values and using the
commodity shares of total materials use as weights.

Although the majority of purchased materials used by
manufacturers are "manufactured" materials, some purchased
materials are sourced from outside the manufacturing sector (for
example, mining materials and agricultural products).79 For
durable goods manufacturers, the total use of these so-called
"non-manufactured" materials is typically quite small. However,
the use of non-manufactured materials is more significant for
several nondurable manufacturing industries, in particular food
product and petroleum and chemicals manufacturing.

Unfortunately, we do not presently have price deflators for most
non-manufactured commodities at a sufficiently detailed level
(either imported or domestic). As such, we were forced to restrict
the estimation approach described in Section 6.2 to manufactured
materials. More specifically, although the BEA does not publish
industry-level manufactured materials price deflators, we construct
them following BEA's methodology for all purchased materials. Given
published values for total purchased materials and our estimate of
total manufactured materials, the implied values for
non-manufactured materials (nominal and real values) are then
backed out residually (via chains-stripping). As with capital,
labor, energy, and services, the implied contribution of
non-manufactured materials to economic growth is held fixed in the
growth accounting simulations discussed below.80

6.4 Offshoring and the Bias to MFP

Table 7 presents our alternative estimates of multifactor
productivity growth in U.S. manufacturing for the period 1997-2007.
The first column restates our baseline MFP results from Section 3,
while column (2)
presents estimates in which all commodities - both domestic and
imported - have been deflated with the unadjusted domestic
deflators provided to us by BEA. Because BEA's domestic deflators
are mostly PPIs, column (2) is labeled
"IPP=PPI". Since our alternative materials deflators are derived
by adjusting domestic commodity prices, the estimates in column
(2) should be
interpreted as the appropriate reference or "jumping-off" point
for gauging the incremental effect of offshoring. In other words,
they show what MFP would be if the rate of price inflation for
imported commodities was the same as for their domestic
counterparts. This assumption is maintained in equation (2) in order to
hone in on the impact of the level difference in prices between
imported and domestic commodities.

For the entire manufacturing sector (row 1), deflating imported
materials with domestic prices serves to reduce MFP growth by a bit
less than 0.1 percentage point, from 1.30 percent in our baseline
scenario, to 1.23 percent. Almost all of this revision stems from
the differences in the deflators for imported and domestic
semiconductors. 81 In other words, prices for imported
semiconductors--a product used heavily by the computer and
electronic products industry--fell less rapidly than their domestic
counterparts. The discrepancies are especially evident in the early
years of our data and appear to be the result of inconsistent
adjustment of imported and domestic semiconductor prices for
quality improvements. Although not the focus of our paper, the drop
in MFP between columns (1) and (2) likely
represents an additional modest bias.82

In terms of the industry-level contributions to overall
manufacturing MFP, almost all of the "IPP=PPI" effect is
concentrated within the computer and electronic products industry,
which is not surprising given it is the largest consumer of high
tech materials: average annual MFP growth in the industry falls
from 6.8 to 6.3 percent after we deflate imported high-tech
materials with their corresponding domestic prices. Moreover, once
we exclude the contribution of the computer and electronic products
industry, overall manufacturing MFP is essentially unchanged from
our baseline estimate under the "IPP=PPI" scenario at 0.67
percent (row 2, column 1 versus column 2).

Columns (4) -
(11) present MFP
estimates that have been further adjusted to account for offshoring
bias. The differences across these columns are driven entirely by
the assumptions we make about the size of the import discount as
discussed in Section 5. In columns (4) - (9), our estimates
of the import discount are informed by our analysis of IPP
microdata, while in columns (10) and (11) we apply
discounts that are roughly consistent with the available case study
evidence.

On balance, for the entire manufacturing sector (row 1), we find
that correcting for offshoring bias lowers MFP growth by an
additional 0.1 to 0.2 percentage point during the 1997-2007 period.
In other words, average annual productivity growth is between 5 and
15 percent less than in column (2) and between
10-20 percent less than our original, baseline estimate in column
(1). These
numbers are fairly significant, as a 0.1 percent average annual
growth rate for multifactor productivity roughly equals the average
annual contribution of the capital stock to manufacturing growth
during this period.

The disaggregate results also highlight the fact that one
industry--computers and electronic equipment (NAICS 334)--account
for most of the growth in productivity in manufacturing over the
decade, although that industry accounted for about 10 percent of
manufacturing's employment and nominal value added. Indeed,
baseline multifactor productivity grew at an annual rate of only
0.69 for manufacturing excluding the computer industry (column 1,
line 2).

Moreover, if we exclude the contribution of the computer and
electronic products industry, correcting for offshoring results in
larger percentage adjustments to MFP which falls from 0.67 percent
in column (2)
to between 0.52 percent (column 4) and 0.63 percent (column 5); in
other words the reduction in MFP widens to as much as 22
percent.

As expected, MFP growth falls the most under the unadjusted
import discount scenario (column 4) and the least under the
structurally adjusted discount scenario (column 5). However, the
differences between these estimates--and also those associated with
our median switching results (columns 6 - 9) are relatively small,
with MFP falling by roughly 0.1 percentage point for both the
structurally adjusted import discount (column 5) and the pooled and
within product switching discounts that control for outliers
(columns 6 and 8), and by about 0.2 percentage point under the
unadjusted discount scenario and the median switching scenarios
that excludes both outliers and rules out positive discounts.

Finally, columns (10) and (11) present MFP
estimates associated with a blanket discount of either 50 or 30
percent for commodities from developing countries and either 30 or
15 percent for commodities from intermediate countries. These
represent discounts on the high and low end, respectively, of those
found in the case study and business literature presented in
Section 5. The results for both of these scenarios remain broadly
consistent with our results based on IPP microdata, with
manufacturing MFP (overall and excluding computers) falling between
0.2 and 0.1 percentage point. Interestingly, after excluding the
contribution of computers and electronic products, the estimates
for our 50/30 scenario in column (11) align quite
closely with those for our unadjusted import discount in column
(4).

6.5 Substitution Bias from Within the Import Price
Index

Although the primary focus of our paper is on offshoring, in
this section we briefly examine whether import price
mismeasurement may also have distorted manufacturing
productivity. Just as input prices fail to fully account for the
savings realized by domestic producers from offshoring, so too may
import prices be overstated if they fail to fully account for
shifts among importing countries (i.e. from advanced to
intermediate countries, intermediate to developing, etc.). If
import price growth has been overstated, then domestic input prices
will be also, along with MFP.83

To investigate this, we adjust for the substitution
within the import price index before passing alternative
import price series through our growth accounting framework. We
correct IPP commodity prices following an analogous approach to our
adjustment for input prices: IPP prices are simply substituted for
domestic prices for the price inflation term in equation (1), and changes in
import shares replace changes in domestic supply.84 Two
alternative import price series were constructed based upon our
unadjusted and adjusted import discounts. Following the same
approach as described in Section 3, we then fold the adjusted
import prices into our baseline growth accounting procedure for
building industry-level intermediate input prices.

More specifically, Fisher-ideal chained index values for
imported materials are computed for each manufacturing industry,
for durable and non-durable manufacturing, and for all of
manufacturing. Real domestic materials values are then derived
residually via chain stripping based on published estimates for
total purchased materials. For our growth accounting estimates, we
then hold the domestic materials prices fixed at the values
estimated in the baseline scenario. Thus, MFP will increase or
decrease directly as a result of changes in the valuation of
imported materials.

The results for import price measurement and its effect on
manufacturing productivity during 1997-2007 are shown in Table 8.
Unlike our correction for offshoring, the effect of import price
mismeasurment on MFP does not "jump off" from the IPP=PPI
scenario. Rather, the appropriate reference points are the
unadjusted, baseline MFP estimates presented in Table 2 (column 2)
and in Table 7 (column 1). These are restated in the first column
of Table 8, while columns (2) and (3) present the MFP
estimates associated with our correction to import prices.

The effect of import price mismeasurement on MFP is considerably
more varied--and therefore less conclusive--than for offshoring.
Using the unadjusted discount, import price mismeasurement would
appear to impart a substantial bias to manufacturing productivity,
with average annual MFP growth falling from 1.30 percent in our
baseline scenario to 1.02 percent. Under this scenario, the bias
from import price mismeasurement is actually somewhat greater than
even our largest estimates of the bias from offshoring. In
contrast, under the adjusted discount scenario, MFP growth is
little changed from the baseline estimates.

Clearly more work on this topic is warranted. However, the
limited evidence we bring to bear on import price mismeasurement
suggests it may provide an important additional source of upward
bias to manufacturing MFP.

7. Offshoring and the Bias to Real Value Added

7.1 Overview and Methodology for Value Added
Simulations

In this section, we present a range of alternative estimates for
real value added growth with the goal of ascertaining the extent to
which offshoring bias may have caused the official estimates to be
overstated. The BEA derives indexes for industry-level value added
using the double-deflation method in which real value added is
computed as the difference between real gross output and real
intermediate inputs (i.e. energy, services, and materials). More
specifically, separate estimates of real gross output and
intermediate inputs are combined in a Fisher index-number formula
in order to generate indexes for value added (see Kim et al,
2008). Thus, if real intermediate input growth is understated as a
result of offshoring bias then real value added growth will be
overstated.

In what follows, we replicate this double-deflation procedure
using our adjusted measures of real purchased materials in place of
the official one. In other words, we derive the implied value of
real value added associated with published measures of real gross
output, energy, and services and our adjusted measures for
purchased materials inputs.85 This was done for all of the private
sector industries in the GDP-by-industry accounts, and for several
aggregates of interest. Our alternative measures of real value
added can then be compared with the published estimates.

7.2 Value added results

The results of this procedure can be found in Table 9, which is
structured nearly identically to Table 7. Estimates of real average
value added growth for 1997-2007 are presented under the same
alternative scenarios we used for MFP. In addition to showing
results for manufacturing, the bottom three rows of Table 9 also
contain estimates for private goods producing industries, private
service producing industries, and all private industries.

Before turning to our adjusted estimates, we first discuss the
baseline results in column (1). These were
derived using our unadjusted materials deflators, the construction
of which was described in Section 6. Because our unadjusted
materials measures uses the imported and domestic commodity values
(nominal and real) provided to us by the BEA--values which feed
directly into their official estimates--it is not surprising that
our baseline estimates are very close to the published
figures.86

Under our baseline scenario, manufacturing value added growth
averaged a robust 3.04 percent per year during 1997-2007 (line
1), a rate of growth nearly 1.25 percentage point larger than for
the entire goods producing sector (line 24), and roughly on par
with the private sector as a whole (line 26). Value added growth
for private service providing industries averaged a somewhat larger
3.5 percent per year.

Nearly two-thirds of the value added growth in manufacturing
originated in the computer and electronics product industry. Once
we exclude this industry, which grew at a whopping 23 percent
average annual rate (line 8), value added growth for the rest of
the manufacturing sector falls to a much less remarkable 0.94
percent (line 2).

As was the case in Section 6, in order to quantify the effect of
offshoring bias on value added, we first need an appropriate
jumping off, or reference, point. Because our offshoring adjustment
involves building input cost measures from domestic commodity
prices (along with import discounts and shifts in the imported
share of domestic supply), this reference point is a set of
estimates in which all manufactured materials--both imported and
domestic--are deflated with domestic deflators. The results of this
adjustment are shown in column (2). The scenario
is once again labeled "IPP=PPI" because most of the domestic
commodity deflators used by BEA are in fact PPIs. Real value added
growth falls from 3.04 percent to 2.82 percent for the entire
manufacturing sector and from 0.94 percent to 0.86 percent for
manufacturing excluding the computer and electronic products
industry.

As with our results for MFP, nearly all of the differences
between the baseline estimates and the "IPP=PPI" estimates
reflect the fact that import prices for high-tech commodities fell
less rapidly than their domestic counterparts. Indeed, we see in
column (3) that
resetting the prices just for these high-tech commodities yields
value added estimates that are virtually identical to those in
column (2). As
such, the industry-level differences between column (1) and column
(2) primarily
reflect differences in the intensity of use of high-tech
commodities. The largest adjustments are seen within durable goods
manufacturing industries, while smaller adjustments occur for
nondurable manufacturing industries and for service producing
industries.

The value added estimates associated with our correction for
offshoring bias are presented in columns (4) -
(11). The
differences across these columns--which are driven entirely by our
assumptions about the import discount--are slightly more pronounced
than for our results on MFP; our results indicate that offshoring
bias has significantly distorted real value added during this
period. For the entire manufacturing sector (line 1), our
offshoring correction reduces value added growth from 2.82 percent
per year to between 2.31 and 2.65 percent per year. In other words,
correcting for offshoring bias lowers manufacturing value added
growth by 7 to 18 percent.

If we exclude the contribution of the computer and electronic
products industry, however, correcting for offshoring bias results
in larger percentage adjustments to value added: average annual
value added growth falls from 0.86 percent in column (2) to between 0.44
percent and 0.75 percent, a significant reduction of between 13 and
49 percent.

As with our results for MFP, value added growth falls the most
if we apply our unadjusted import discount, (column 4) and the
least if we apply the adjusted discount (column 5). The (50/30)
discount simulation (column 11) informed by our case study evidence
yields estimates that are again quite close to the unadjusted
estimates, while a smaller discount of 30 percent for developing
country commodities and 15 percent for intermediate country
commodities yields adjusted value added estimates that are somewhat
closer to the structurally adjusted ones. Among the four sets of
estimates derived with the import discounts from our median
switching results, manufacturing value added excluding the computer
industry is reduced by a substantial 30 to 48 percent.

A final point to note is that, after correcting for offshoring
bias, it appears that the value added growth in the U.S.
manufacturing sector lagged considerably behind that of the private
sector as a whole. Moreover, the wedge between service sector value
added growth and manufacturing sector value added growth now
appears wider, increasing from three-quarters of a percentage point
in column (2)
to between 1 and 1¼ percentage points in columns (4) - (11).

8. Conclusion

This paper brings a variety of data to bear on the question: are
our measures of import prices, input costs, and hence value added
and productivity systematically biased by the increased incidence
of offshoring? Taken as a whole, our findings suggest that both
productivity and value added have indeed been overstated due to the
failure of statistical agencies to capture level differences in
prices associated with shifts in sourcing from domestic to foreign
suppliers. In other words, multifactor productivity has been a
somewhat less important driver of manufacturing output growth,
while trade liberalization has played a larger role. Our
application of a formula-based correction to this offshoring bias
extends the empirical literature on outlet substitution in the CPI
to both input and international prices, and confirms evidence by
Reinsdorf and Yuskavage (2009) and Nakamura and Steinsson (2009)
that the IPP is missing important information at the point of item
substitution.87 Our results imply that the
concurrent rise in output and fall in employment at U.S.
manufacturing establishments are not entirely at odds; rather an
important portion of value added growth simply reflects price
declines in imported intermediate inputs not captured by official
statistics.

Similar biases, however, may also arise from the offshoring of
other inputs and affect statistics for other sectors and for the
aggregate economy. For instance, in the 2000s, sizable import
penetration by developing countries occurred in computers and
machinery products, which are largely treated as capital inputs in
the industry accounts. Price drops accompanying the substitution of
imported for domestic capital equipment would not be captured in
capital price deflators, possibly leading to an understatement of
the growth of capital services and an overstatement of growth in
multifactor productivity.88 The same problem arises from
services offshoring. Collecting accurate price information on
services trade is complicated by the fact that the level of detail
in services sector data is quite limited (Sturgeon et al. 2006,
Norwood et al. 2006, Jensen 2009) and that the BLS international
prices program does not cover business services imports and
exports. Identifying incremental sources of offshoring bias in
productivity measures is a promising area for future research.

There may also be some overlap in the offshoring effect we
identify empirically and the impact of new traded varieties
measured in Feenstra, Mandel, Reinsdorf and Slaughter (2009). In
principle, new traded varieties and those specifically due to
offshoring are observationally equivalent, so while the two sources
of bias in prices are largely complimentary in the way that they
are measured, we do not view our results and those in Feenstra et
al. as strictly additive.

Finally, in principle it is possible to correct for this bias
directly without resorting to a formula-based approximation as we
do here. Alterman (2009) has proposed the construction of an input
price index based on a survey of purchasers, which if implemented
by BLS, would address the biases to the industry statistics from
all shifts in sourcing. The proposed index, which would not
distinguish source country, would capture price changes from shifts
in sourcing among domestic suppliers, among domestic and
international suppliers, and among international suppliers.

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Appendix 1. Comparability of Baseline MFP Results with
Published Values

This section provides a comparison of our baseline MFP results
to the manufacturing productivity estimates published by
BLS.89 Appendix table 2 and appendix figure
1 display our estimates of MFP growth for manufacturing along with
the contributions to growth of the various factor inputs (i.e.
capital, labor, materials, energy, and services). The columns in
appendix table 2 present average annual growth rates for the
entire 1997-2007 period, as well as several different sub-periods,
while the first three rows of the table present, respectively, the
MFP measure used in this paper, the BLS MFP measure, and the
difference between the two measures.

According to BLS, manufacturing MFP growth averaged 2.2 percent
per year for the 1997 to 2007 period, while our measure of MFP
expanded at more moderate, 1.3 percent pace. As described below,
roughly one-third of the 0.9 percentage point discrepancy between
the two MFP measures appears to be driven by different vintages of
source data. Methodological differences, both with respect to the
measurement of output and the measurement of factor inputs, appear
to explain most of the remaining discrepancy.

As discussed in Section 3, BLS estimates manufacturing MFP
annually using the concept of sectoral output, which is defined as
sales to final users plus deliveries to other industries
outside the manufacturing sector.90 The BLS measure of
intermediate inputs also differs from our measure in that it
excludes intermediates inputs sourced from within the manufacturing
sector. In other words, while an imported auto part would be
included in the both our measure of intermediate inputs and the BLS
measure, the BLS measure would not include an identical,
domestically-sourced part. However, it is important to note that
although these methodological differences should result in
substantial level differences in output and intermediate
inputs, because BLS uses fixed adjustment factors to convert gross
output to sectoral output and to net out intra-sector
intermediates, the growth of our series and the BLS should,
in principle, be approximately equal.91

Line 4 in appendix table 2 shows the difference between the two
output measures. For the entire period, BLS output grew 0.37
percentage point faster per year, on average, than our output
measure. As can be seen in the upper panels of appendix figure 1,
most of this discrepancy is due to the substantial difference in
output growth in 2007. As of this writing, BEA has yet to fold the
2007 Census of Manufactures (CM) into the annual industry accounts
data which we use for our analysis. In contrast, BLS have
incorporated the 2007 CM data into their output measures, although
given the Census Bureau has not fully released all the accompanying
detail for the 2007 CM, this may lead to future revisions in the
BLS measure.

If we exclude 2007 from our comparison, the difference between
the two output measures becomes much smaller as we would expect,
and stands at just 0.1 percentage point. This in turn leads to
smaller gap between the BLS MFP measure and our own measure, which
shrinks from 0.9 percentage point to 0.6 percentage point.

Excluding 2007, most of the difference in average MFP growth
therefore appears to be driven by differences in input measurement.
Line 5 and lines 5a through 5e present the differences between the
contributions of the various factor input measures (i.e. the
growth rates weighted by their cost shares). For 1997 to 2006, the
total factor input differential is 0.57 percentage point (line
5). Among the various factor inputs, the labor and materials
contributions differ consistently across sub-periods and combine to
explain most of total input difference.92

In terms of the labor contribution, although we attempt to
control for changes in labor quality, the BLS do not, a fact which
likely explains entire difference shown in line 5a.93 As
can be seen in appendix figure 1, although both series exhibit a
very similar contour, our labor contribution measure falls less
than the BLS measure during the 2000-2001 recession, consistent
with the observation that labor quality tends to move
counter-cyclically.

For the materials contribution, the 0.35 stronger growth we
observe for our measure in line 5e likely owes to differences in
the weight used to compute the contribution of materials to output
growth. Because the level of our materials measure exceeds the
level of the BLS materials measure (again, the growth rates should
be roughly equivalent), the resulting cost share for materials
should also be greater. That said, as can be seen in appendix
figure 1, the general contour of our purchased materials
contribution is very similar to the BLS one between 1997 and
2007.

Line 6 presents the remaining difference between the two MFP
measures after accounting for differences in output and input
measurement. This residual is quite small, averaging roughly 0.1
percentage point in each of the sub-periods. As a final robustness
check, we also calculated MFP estimates based on a sectoral output
concept (i.e. excluding intra-sector shipments from our output and
intermediate inputs measures). As shown in line 7, if we ignore the
data vintage issue for 2007, the resulting MFP estimate is also
quite close to the BLS measure, with the average annual growth
differing by less than 0.1 percentage point during 1997 and
2006.

Diewert and Nakumra (2009) develop a three-sector, two-period
model to demonstrate the bias imparted to an input price index from
outsourcing (see pp. 15-18 of their work). They define the "true"
index for period 1 as the ratio of the correct (unit) price in
period 1 to the price in period 0 in the case where a domestic
producer (sector 3) has switched all or part of its sourcing of
inputs from a high-cost supplier (sector 1) to a low-cost supplier
(sector 2):

(A1)

Diewert and Nakamura show (pp. 18) that the true period 1 price
index can be restated as follows:

(A2)

where d and
are price discount for the
low-cost supplier (relative to sector 1) and the gain in market
share (previously zero), and i+i is the underlying rate of
inflation for the high-cost supplier (sector 1):

(A3)

The rate of inflation faced by sector 3 is assumed equal to the
underlying rate of inflation in the high-cost producer (which is
assumed equal to the rate of inflation for the low-cost producer),
or the ratio of the high cost supplier's prices in period 1 and
period 0.

Substituting, the correct price in period 1, , may therefore be expressed as:

(A3')

We modified the above expression to account for the base period
indexes and multiple time periods in our data. Importantly, the
rate of inflation for the high cost producer between any two
periods is defined as the ratio of the unadjusted (biased) index,
I, in period t to the index in period t-1:
. In the absence
of any outsourcing/offshoring bias, one can express the index in
period t, It, as the product of last period's index and the
rate of inflation (1+i):

(A4)

Applying the same logic as in equation A3', one can therefore
approximate the "true" input price index, IT, as:

Where ppreli_d and ppreli_i are the price
relatives for developing and intermediate countries respectively
(relative to advanced countries), share_qd and
share_qi are the shares of developing and intermediate
imports in total domestic consumption, and 1+pctchng is the
rate of inflation for the high cost (i.e. domestic) intermediate
producer. The price relative for advanced countries is assumed
equal to one, i.e. we assume there is no discount associated shifts
in sourcing from a domestic supplier to a supplier from an advanced
country.

Consider the following numerical example: Suppose the rate of
inflation is 2 percent, so 1+i = 1.02. Developing countries gain
market share (quantity terms) of 1 percent (.01) and the discount
is 50 percent (.5). For simplicity, assume also that the
intermediate countries' share is constant. Then the true rate of
inflation is 1.02 - 0.0051= 1.0149. Last period's corrected index
would be multiplied by 1.0149 to generate this period's corrected
index.

Finally, although the share terms in the Diewert and Nakamura
(2009) correction are explicitly defined as quantity (or physical)
shares, we lack specific information on these variables. However,
these quantity shares can in fact be expressed as a function of
their corresponding expenditure shares and price discounts,
variables for which data are available. Define the price relative
for developing countries as
, and the price relative for
intermediate countries as
. These are just the
ppreli_d and ppreli_i terms in equation (A6).
Rearranging terms, we have
and
.

Omitting time subscripts, expenditure shares for advanced,
developing, and intermediate countries (sea, sed, sei) at any point
in time are:

It is straightforward to show that the quantity shares
may then be expressed as functions of dd, di, sea, sei, and
sed:

This is the approach we follow when estimating equation A6 in
the paper.

Footnotes

** This research was supported with funding from the Bureau of Economic Analysis and the Alfred P. Sloan Foundation and was conducted with restricted access to Bureau of Labor Statistics (BLS) import price data as well as unpublished detail on imported materials provided to us by the Bureau of Economic Analysis (BEA). The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff of the Board of Governors, the BLS, or the BEA. We thank Jonathan Collins and Lillian Vesic-Petrovic for research assistance and the participants of the NBER-CRIW 2010 Summer Institute, the conference on Measurement Issues Arising from the Growth of Globalization, Washington, DC, November 2009 for comments and suggestions. We are especially grateful to Bill Alterman, Erwin Diewert, Nicole Mayerhauser, Emi Nakamura, Alice Nakamura, Marshall Reinsdorf, Erich Strassner, Steve Rosenthal, and Rozi Ulics for their invaluable feedback and assistance on this project. Return to
text

1.
Expressed as a percent of GDP, imports rose by roughly 5 percentage
points from 12½ percent of GDP in 1997 to 17½ percent
in 2008, while exports as a share of GDP increased only marginally.
In 2007, China became the largest exporter of goods to the United
States, surpassing Canada. Return to
text

2.
See, for example, Executive Office of the President (2009), Pisano
and Shih (2009), New America Foundation (2010), Helper (2008),
Pollin and Baker (2010), and the Surdna Foundation
(2010). Return to text

3.
With the Bureau of Economic Analysis's May 2010 comprehensive
revision to the Annual Industry Accounts, manufacturing output now
expands at a slightly faster rate during this period. The analysis
throughout this paper is based upon the previous vintage of these
data published in 2009. Return to
text

4.
Michael Mandel makes note of this phenomenon in his June 3rd, 2009
Business Week article, "Growth: Why the Stats are
Misleading." Return to text

5.
See Diewert (1998), Hausman (2003), and Reinsdorf (1993) on biases
to the CPI arising from outlet substitution bias and Houseman
(2008) and Diewert and Nakamura (2009) on the relationship between
outlet substitution bias and biases to input price indexes arising
from shifts in the sourcing of inputs. Return to text

6.
See "In Recession, placecountry-regionChina Solidifies its Lead in
Global Trade," The New York Times, October 14, 2009. Return to text

7.
Similarly, see Kurz and Lengermann (2008) and Yuskavage, Strassner,
and Medeiros (2008) for empirical evidence on the increased foreign
sourcing of intermediate inputs. Hummels, Ishii, and Yi (2001),
Feenstra (1998), Yeats (2001), and Campa and Goldberg (1997) also
discuss the issue. Return to text

8.
In addition to cost savings, risk sharing and specialization are
also factors that play a role in a firm's decision to outsource or
offshore. See Abraham and Taylor (1996). Return to text

9.
Bils (2009) finds evidence in the CPI that two-thirds of the price
increases for new-model substitution can be attributed to quality
upgrading. The objective of our structural approach is to adjust
for possible level differences in quality across
countries. Return to text

10. Reinsdorf and Yuskavage (2009) reach a
similar conclusion. Moreover, results from Feenstra (1994) and
Broda and Weinstein (2006) indicate the demand between imported
varieties tends to be elastic. Return to
text

12. The extraordinary growth in the
computer industry, in turn, largely reflects rapidly dropping
prices, which for several component industries have been adjusted
using hedonic methods to account for rapid technological
improvements to products. Return to
text

13. That said, the late 19th century
witnessed a level of global integration that in some ways remains
unsurpassed. See O'Rourke and Williamson (1999). Return to text

14. In terms of services trade, in 2008,
BEA data on the trade in services indicates that 59 percent was
travel, transport, royalties, and education-related, while the
remaining 41 percent was business services. Return to text

15. See Hummels, Ishii, and Yi (2001) for
the seminal work that measures the increase vertical integration
using input-output tables from the OECD and emerging market
countries. The authors estimate that vertical specialization
accounts for up to 30 percent of world exports, and has grown as
much as 40 percent in the last twenty-five years. Return to text

16. We classify countries with less than
20 percent of U.S. per capita GDP as developing, and, with a few
exceptions, countries with per capita GDP equal to or exceeding
two-thirds that of that in the United States as advanced. The
remaining countries are classified as intermediate. We classify the
Middle East oil-producing countries as intermediate, although per
capita GDP exceeds two-thirds of U.S. per capita GDP on account of
their oil revenues. In addition, we classify Singapore, Hong Kong
and Brunei as intermediate although in recent years their per
capita GDP has been at or somewhat higher than our cut-off level.
Below we report evidence of large differences in observed price
levels of imports from these countries and those from advanced
countries within detailed product categories, which provide a
justification for classifying these borderline countries as
intermediate. A comprehensive list of countries by category is
provided in Appendix Table 1. Return to
text

17. The data in Table 1 portray the number
of plants in a particular size class at two points in time and
represent net changes: plant openings, plant closures, and changes
in plant size that result in a particular plant being reclassified
into a different size category. Return
to text

18. Although the average growth of
manufacturing has been fairly close to that of the economy as a
whole, the sector has typically exhibited greater cyclical swings.
As a result, the sector tends to make outsized contributions to
changes in GDP growth during economic turning points (Corrado and
Mattey, 1997). In addition, the relatively faster gains in
manufacturing productivity have resulted in lower relative goods
prices which, in combination with inelastic demand for goods (on
average), has led to a decline in manufacturing's share of nominal
output. Return to text

20. This perspective is illustrated by
Executive Office of the President (2009), which emphasizes the
strength of output and productivity growth of U.S. manufacturers
vis-à-vis the aggregate economy and manufacturers in other
industrialized countries and which largely attributes the
employment declines to productivity growth. Recent articles in the
popular press also have advanced this view (e.g. Sara Murray, 2009.
"U.S. Manufacturing Productivity Jumps." Wall Street Journal,
October 23, pp A2 and "Industrial Metamorphosis" The Economist,
September 29, 2005). Return to
text

22. Materials inputs consist of
agricultural, mining, manufactured, wholesale trade, and
transportation. In 2007, 63 percent of all materials inputs used by
the manufacturing sector consisted of manufactured materials. The
KLEMS intermediate use estimates are published for 1998-2007. We
impute the 1997 estimates for the decomposition of intermediate
inputs. Return to text

23. Information technology is defined as
computers, communications equipment, and software. The tabulated
results do not include the decomposition of capital between IT and
other capital as the focus of this paper is on the contribution
from foreign intermediates. Return to
text

24. See Appendix B of Corrado, et al.
(2006) for more details. Pre-1998 unpublished data on hours worked
at the detailed SIC level are controlled to be consistent with
recent NIPA releases and concorded to a NAICS basis. The levels of
the BEA/BLS NAICS employment series are then adjusted in all years
to conform to the industry composition in the Census Bureau's
County Business Patterns data. Next, hours worked at the industry
level are derived which embody the adjusted employment levels but
preserve the implied workweek in published series. Finally, all
adjusted data are controlled so that they sum to the published
BEA/BLS estimates for employment and hours worked in total private
industries. Return to text

25. We extend our gratitude to Erich
Strassner, George Smith, Sue Okubo, and others at BEA for providing
us with the confidential microdata on imported intermediates. The
1997 and 2002 imported intermediate values (import matrices) are
publically available. Return to
text

26. The imported values we employ are I-O
based, or after redefinitions. Redefinitions occur when secondary
products have an input structure that differs substantially from
the primary product input structure. The BEA "redefines" by
moving secondary products from the industry in which it is produced
to the industry in which it is primary. Redefinitions allow the
resulting input-output tables to conform to the
"commodity-technology assumption," consistent with a homogeneous
input-output structure. In addition, outside of benchmark years,
the detailed import values are derived from annual industry
accounts, which rely on the "constant industry technology"
assumption, or that the real use of total intermediates relative to
an industry's gross output has not changed from the prior
year. Return to text

27. Domestic supply represents the total
amount of a commodity available for consumption, i.e., domestic
output plus imports less exports. Using this assumption to
calculate the industry-level estimates implies that all variability
of import usage across industries reflects the assumption and is
not based on industry-specific information. Return to text

28. The import comparability assumption is
described further in Yuskavage, et al (2008) and critiqued in
Feenstra and Jensen (2009). Return to
text

30. See Jorgenson, Gollop, and Fraumeni
(1987) and Hulten (2009) for more on growth accounting methodology,
its early development, and current applications. Return to text

31. Sectoral output is defined as the
gross output of an industry or sector less the amount produced and
consumed within that industry or sector. While the sectoral output
approach is useful for measuring the contribution of foreign
intermediates to overall growth, in this framework the contribution
from imported intermediates and other intermediates are not
comparable, as domestic intermediates only contain inputs purchased
from outside of the sector (see Domar, 1961 and Hulten, 1978).
Similarly, Jorgenson et al. (2005) favor a gross-output approach
over a value-added one so that the contribution of intermediate
inputs to output growth may be identified. Return to text

32. The definitions and notation presented
here are similar to those presented in Corrado, et al.
(2007). Return to text

33. We do not decompose purchased services
into domestic and foreign components. Services imports as a share
of total imports remained constant over the time period 1997 to
2007. Return to text

34. This adjustment is necessary because
the compensation measure in the GDP-by-industry accounts only
includes employees whereas our hours measure includes both
employees and self-employed workers. Return to text

35. Chain stripping (or chain
disaggregation) involves solving for the index residual (i.e. the
price index for domestic materials) when an aggregate exists (the
price index of total purchased materials), and one "child" exists
(the price index for imported materials). Return to text

36. The presentation of the baseline
results closely follows previous work by Kurz and Lengermann
(2008). Return to text

37. As noted, the growth accounting
results in Table 2 reflect the authors' calculations and rely on a
differentmethodology than what is used by BLS. However, many of the
salient features of the data are also observed in the BLS
estimates. See Appendix 1 for a comparison and reconciliation of
our baseline results with those published by BLS. Return to text

38. i.e., NAICS 334 which includes the
production of computers, semiconductors, and communications
equipment. Return to text

39. The growth of imported intermediate
inputs, to some degree, will also reflect the direct substitution
of imported goods for domestic labor and capital. To see this,
consider the case in which a firm previously produced an
intermediate input and final product internally, but now sources
that input from a foreign supplier. In this instance, gross output
will not change, but imported materials inputs will rise and the
labor and capital previously used to produce the input will
fall. Return to text

40. Similar findings have been reported
in other studies. See, for example, Oliner and Sichel (2000) and
Jorgenson, Ho and Stiroh (2008). See also Oliner, Stiroh, and
Sichel (2007) and Syverson (2010) for in-depth reviews of recent
research on U.S. productivity growth. Return to text

41. Throughout the decade, the computer
industry's share of manufacturing value added remained relatively
constant at around 10 percent. In spite of the rapid value added
and MFP growth in this sector, the trade deficit within this
product group greatly widened during the decade and substantial
offshoring of components of the industry occurred (Brown and Linden
2005, Linden, Dedrick and Kraemer 2009). Return to text

42. BLS uses hedonic methods to adjust
prices in the computer industry. For a review of these, see
Wasshausen and Moulton (2006). Return to
text

43. This could also occur if a firm
imports an intermediate input it previously produced internally. In
this case, output will not change but the labor input used to
produce that intermediate input will fall. Return to text

44. See Cavallo and Landry (2010) for a
discussion of imported capital goods, and Yuskavage, Strassner, and
Maderios (2008) and Eldridge and Harper (2009) for estimates of
services offshoring. Return to
text

45. For more information on the BLS price
index computations see Chapters 14 and 15 in the BLS Handbook of
Methods (2009). Return to text

46. For imports, the preferred price
basis for the BLS is f.o.b., or the price "free on board" at the
foreign port of exportation. Return to
text

47. More precisely, the unit of
observation is the ratio of a relative item price in a given period
(relative to the item's price in a base year) to the item's
relative price in the previous month. Return to text

48. The weights for the upper-level
indexes have changed annually since 2001. Prior to the annual
updating, the weights changed infrequently. For instance the 1997
index used 1995 weights, and the 1993-1996 indexes used 1990
weights. Return to text

49. By definition, an industry's value
added equals its gross output minus its consumption of
intermediates. The chain-type quantity index for an industry's
value added is prepared by deflating the current-dollar commodity
measures of gross output and intermediate inputs with the
corresponding commodity price indexes and combining the resulting
commodity quantity indexes of gross output and intermediate inputs
by industry in a Fisher index-number formula Return to text

50. See, for example, Diewert (1998) and
Hausman (2003) for expositions of the categories of price
measurement problems in the CPI. Return
to text

51. Hausman (2003) points out that the
biases from new goods or varieties and from new suppliers or
outlets are first order effects, while substitution bias arising
from the incorrect weighting of price observations is a second
order effect. Return to text

53. Because of the rapid entry and market
share expansion of low-cost suppliers from developing countries in
recent years, the empirical focus of this paper is on price biases
arising from offshoring. However, it should be noted that the
producer price index and the input price index also would be biased
with the entry of a new, low-cost domestic suppliers of
intermediate inputs. The relevant price change is the discount the
new supplier offers, which will not be measured even when the new
entrant is introduced into PPI sample because the index is
constructed from observations on period-to-period changes in the
sales price received by individual suppliers. Return to text

54. A complete derivation of equation 2
can be found in the Appendix 2. Return
to text

55. See Aizcorbe, Corrado, and Doms
(2003) for an exposition of this argument. Return to text

56. Similarly, Besedes and Prusa (2006)
analyze publicly available import data at the product level and
show that the median spell of imports lasts only about 1 year,
while 70 percent of import trade spells last roughly 2 years.
Return to text

57. Although we have focused on the
substitution of low cost foreign for domestic inputs because of the
recent empirical importance of offshoring, the entrance and market
share expansion of low-cost domestic suppliers is an important
aspect of firm dynamics in the United States and also would impart
biases to price indexes. See Foster, Haltiwager, and Syverson
(2008) for evidence that entrants, on average, have higher physical
productivity and offer lower prices than incumbent
firms. Return to text

59. Since the international and domestic
data are merged at a slightly higher level of aggregation, here
commodities are defined as 4-digit NAICS codes. Return to text

60. The narrowly defined groups in this
instance are Harmonized System 10-digit codes, which we assume
contain varieties that are substitutable. This classification is
widely viewed as the most similar, observable categorization among
traded products. As mentioned above, existing estimates of
elasticity of substitution among varieties within HS10 groups are
typically found to be elastic. Return to
text

61. The IPP has its own internal
classification scheme called classification groups, which are
slightly more aggregate than the HS10 codes. The purpose of those
groups is to combine related categories where sampling is
relatively sparse to form an appropriate mass of imports. In the
majority of cases classification groups map uniquely to HS10 codes,
and in the majority of the remainder to only two HS10 codes, and so
we use HS10 to describe both types of category. Return to text

62. Other item fields include shipping
information, price collection details as well as flags for transfer
prices and missing value imputations. In certain instances where
comparability is feasible, instead of starting a new series for a
new item, IPP staff will replace an item and make an adjustment to
the price. These types of adjustment account for about 1 percent of
the price observations and are treated as real prices.
Return to text

63. Missing price relatives are set equal
to 1, implying an import price discount of zero. Return to text

64. The assumption of fixed weights
across time does not significantly alter our qualitative or
quantitative results below. Return to
text

65. These items could either be newly
consumed varieties or simply newly sampled items in the IPP survey.
Return to text

66. Table 4 also contains information on
the number of country source switches per income level pair.
Notably, there is a lot of within group switching (e.g. developing
to developing, advanced to advanced, etc.), accounting for 42
percent of all switches. The other 58 percent have more high to low
income switches than the other way around, with the ratio of
observations in the upper triangle of the cross tab (i.e., high to
low switches) to the lower triangle (i.e., low to high switches)
equal to 1.24. That is, there are 24 percent more high to low
income switches. That ratio is more pronounced in the second half
of the sample with 32 percent more high to low switches than low to
high. Return to text

67. Since the cell sizes for the country
switches are small, we compute the median discount for each country
and NAICS product group. Return to
text

68. In order to use this richer measure
of product-level firm size which is only available for the U.S., it
is assumed that the curvature of the firm size distribution is the
same in the rest of the world. If the underlying size distribution
is the same function from the power law family of distributions,
this assumption is satisfied even if productivity and size levels
are very different across countries. Moreover, this measure of firm
size is exogenous to the price distribution of U.S.
imports. Return to text

69. See Mandel (2010) for descriptive
statistics on the classification scheme and for additional detail
on the estimation technique. For other variations on this
identification strategy, see: Hallak and Schott (2008), Khandelwal
(2009) and Baldwin and Ito (2009). The resulting product
classification scheme covers approximately 1,100 HS6 codes for U.S.
imports. Return to text

70. Recent empirical studies suggest that
this covariance may be negative due to the positive links between
exporter income and export prices (see, for instance, Schott (2004)
or Hummels and Klenow (2005)); the implication is that richer, more
productive countries export higher quality items, but with lower
marginal costs per unit of quality. On the other hand, theoretical
frameworks in which markups vary across producers suggest that this
covariance may be positive. Under general specifications of
industry demand such as the translog expenditure function, higher
productivity exporters obtain a higher market share, and hence
charge a higher markup over marginal cost. The partial effect of
markups would be to increase quality-adjusted prices of the higher
productivity, higher quality exporters, offsetting at least in part
the marginal cost effect. Return to
text

71. Since quality variance measures are
only available at the HS 6-digit level of aggregation, we estimate
robust standard errors clustering HS10-country groups within HS6
categories. Return to text

72. In the left panel, for all exporters
the relationship is significant and positive, and particularly
strong for the quadratic term; the variance of prices increases at
an increasing rate with the variance of quality. Given that the
relative prices are in reference to the advanced set by
construction, product variance is better described by the relative
prices of only the developing and advanced countries. The right
panel shows results for that specification and, indeed, the
estimates are larger for both the linear and quadratic
terms. Return to text

73. Though we find the quality-adjusted
discount to remain negative in most cases, the discount could be
positive (i.e., a premium) for several reasons. On the one hand,
for an identical good we would expect to find that developing and
intermediate countries have higher prices relative to advanced
countries in industries where advanced countries have a
productivity advantage. Moreover, the premia may be partly
attributable to the way IPP collects its prices; IPP aims to
collect prices 'at the dock' and net of tariffs. Therefore, if
tariffs have been going down for developing countries (note that
China's accession to the WTO as well as the expiry of the MFA are
contained in the sample period), we might not observe a change in
their IPP price even if imports increase. Return to text

74. See "Exporting Work: Outsourcing
That Once Sent Low-Skill Jobs to Mexico Is Now Sending Some of San
Diego's High-Skilled, High-Wage Jobs to India, China and
Elsewhere." San Diego Tribune, April 4, 2004. Return to text

75. Thus, we are again assuming here that
the price discounts for products from developing and intermediate
countries relative to their U.S. counterparts are the same as for
advanced foreign economies. Return to
text

76. While this is slightly more aggregate
than the BEA product codes described in Section 3, which are NAICS
6-digit classification codes, the concordance between the
Harmonized System categories used by the BLS at the micro-level and
NAICS was greatly simplified by aggregating slightly. Moreover,
given the sampling frame of the IPP, certain products have
relatively sparse data giving rise to noisy estimates of the import
discount. Aggregating to the NAICS 4-digit level also serves to
smooth through the product-level volatility in the discount
estimates. Return to text

77. Cumulative input cost inflation is
computed as the percent change between the index values in 2007 and
1997. Commodity level inflation is computed in similar fashion at
the NAICS 3-digit level. Included in the overall manufacturing
number but excluded from the chart are the following commodities:
petroleum products, computer and electronic components. Petroleum
products had cumulative input cost inflation of 137 percent and
bias-corrected inflation of 134 percent. Computer and peripherals
had input costs decline by 35 percent, 51 percent
adjusted. Return to text

78. These tables show the use of four
digit commodities by each of the 65 industries in the
GDP-by-Industry accounts, with commodities categorized as energy,
materials, or services. These data are available for download at
http://www.bea.gov/industry/xls/KLEMS_intermediate_use_1998_2007.xls.
Return to text

79. Manufactured materials comprise
roughly 60 percent of total materials and exclude farming, mining,
wholesale and retail trade, and transportation. In durable
manufacturing industries, the manufactured materials share of total
purchased materials is approximately 80 percent, while in
nondurable industries it is approximately 50 percent. The share for
nondurable manufacturing industries is held down primarily because
of two industries: food products manufacturers are heavy users of
agricultural commodities, while petroleum and chemicals industries
consume large quantities of mining materials. Return to text

80. We therefore likely continue to
overstate MFP in industries where the offshoring of
non-manufactured material inputs has been pervasive. Return to text

81. Indeed, the estimates in column
(3)--where we only reset import prices for commodities in this
category--are virtually identical to those in column (2). Resetting
import prices for all commodities except high-tech yields MFP
estimates that are very close to the baseline estimates in column
1. Return to text

82. Because of the high import
penetration in semiconductors and other high-tech products,
consistently adjusting domestic and import prices for product
improvements is important for the accuracy of industry and national
income statistics, though difficult owing to lack of product
detail, particularly for imports. Addressing this problem has
resulted in substantial revisions to the national accounts
statistics in the past (Grimm, 1998). Return to text

84. In addition, the correction was
applied to IPP prices at the NAICS 6-digit product (commodity)
level rather than at the 4-digit level that was used for our input
price adjustment. Return to text

85. As discussed in Section 6.2, in
practice we were only able to adjust our baseline measure of
manufactured material inputs. Our baseline estimate of
non-manufactured materials was held fixed in all of the value added
simulations along with the published values for gross output,
energy, and services. Return to
text

86. This was not the case for our
baseline estimates of MFP. As discussed in section 3, our approach
to estimating productivity differs somewhat from that of the
BLS. Return to text

87. Reinsdorf and Yuskavage (2009)
examine pricing in selected consumer goods and provide preliminary
evidence of biases to GDP from import growth. Biases to price
indexes from offshoring and their implied biases to GDP growth also
have been covered in the business press. (See Michael Mandel, "The
Real Costs of Offshoring," Business Week, June 18, 2007, and
Michael Mandel, "Growth: Why the Stats Are Misleading," Business
Week, June 3, 2009.) Return to
text

88. On the other hand, real value
added--by definition--would not be affected by the mismeasurement
of the imported price of capital equipment. Return to text

92. Over the full 1997-2006 period, our
capital contribution is about 0.1 percentage point smaller than the
BLS one. Although we adopt a very similar approach to the BLS for
estimating capital services, BLS include land in their measure,
along with equipment, structures, and inventories, while we do not.
The differences for energy and services are trivial. Return to text

93. Indeed, our unadjusted hours growth
measure, which is not shown in appendix table 2, is identical to
the BLS one. Return to text